Methods and systems for using artificial intelligence to analyze user activity data

ABSTRACT

A system for using artificial intelligence to analyze user activity data, the system comprising a computing device configured to receive from a user, at least a biological extraction and at least a user activity datum, determine a current user location, generate a diagnostic output as a function of the biological extraction, wherein the diagnostic output comprises a condition of the user, retrieve, from a fingerprint database, at least a datum of user fingerprint data, identify a plurality of compatible elements at the current user location as a function of the condition of the user, select at least a compatible element as a function of the fingerprint data, and present, via a graphical user interface, the at least a compatible element to a user device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of Non-provisionalapplication Ser. No. 16/532,283 filed on Aug. 5, 2019 and entitled“METHODS AND SYSTEMS FOR USING ARTIFICIAL INTELLIGENCE TO ANALYZE USERACTIVITY DATA,” the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention generally relates to the field ofmachine-learning. In particular, the present invention is directed tomethods and systems for using artificial intelligence to analyze useractivity data.

BACKGROUND

Accurate selection of compatible elements as a function of analysis ofuser data can be challenging. Under some circumstances, accuratelyselecting and recommending compatible elements may be of utmostimportance. Incorrect selection of compatible elements may lead to errorand user dissatisfaction.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for using artificial intelligence to analyze useractivity data, the system comprising a computing device configured toreceive from a user, at least a biological extraction and at least auser activity datum, determine a current user location, generate adiagnostic output as a function of the biological extraction, whereinthe diagnostic output comprises a condition of the user, retrieve, froma fingerprint database, at least a datum of user fingerprint data,identify a plurality of compatible elements at the current user locationas a function of the condition of the user, select at least a compatibleelement as a function of the fingerprint data, and present, via agraphical user interface, the at least a compatible element to a userdevice.

In an aspect, a method for using artificial intelligence to analyze useractivity data, the method comprising receiving, by a computing device,from a user, at least a biological extraction and at least a useractivity datum, determining, by the computing device, a current userlocation, generating, by the computing device, a diagnostic output as afunction of the biological extraction, wherein the diagnostic outputcomprises a condition of the user, retrieving, by the computing device,from a fingerprint database, at least a datum of user fingerprint data,identifying, by the computing device, a plurality of compatible elementsat the current user location as a function of the condition of the user,selecting, by the computing device, at least a compatible element as afunction of the fingerprint data, presenting, by the computing device,via a graphical user interface, the at least a compatible element to auser device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for using artificial intelligence to analyze user activity data;

FIG. 2 is a block diagram illustrating an exemplary embodiment of adiagnostic engine;

FIG. 3 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 4 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of acompatible label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of afingerprint database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of abehavior database;

FIG. 9 is a block diagram illustrating an exemplary embodiment of aclassification database;

FIG. 10 is a block diagram illustrating an exemplary embodiment of acompatible element database;

FIG. 11 is a block diagram illustrating an exemplary embodiment of afirst label learner;

FIG. 12 is a block diagram illustrating an exemplary embodiment of aproduct category list;

FIG. 13 is a block diagram illustrating an exemplary embodiment of agraphical user interface;

FIG. 14 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of acompatible element similarity index value database;

FIG. 16 is a process flow diagram illustrating an exemplary embodimentof a method of using artificial intelligence to analyze user activitydata;

FIG. 17 is a process flow diagram illustrating an exemplary embodimentof a method of generating compatible elements; and

FIG. 18 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for using artificial intelligence to analyze useractivity data. In an embodiment, at least a server receives at least abiological extraction from a user and at least a user activity datum. Atleast a server retrieves from a fingerprint database at least a datum ofuser fingerprint information. User fingerprint information may includefor example a previous user activity datum, including patterns, and/orbehaviors thereof. User fingerprint information may include previoussearch queries that a user may have generated. At least a serverclassifies the at least a user activity datum. Classification may becustomized to a particular user and based on a particular user'sfingerprint information. Compatible elements may be generated andfiltered as a function of the user's location and how each compatibleelement will affect the user's condition. At least a compatible elementis then transmitted by at least a server to a user client device. In anembodiment, system may generate an audiovisual notification to alert auser to a compatible element at the user's location.

Turning now to FIG. 1, an exemplary embodiment of a system 100 for usingartificial intelligence to analyze user activity data is illustrated.System 100 includes at least a server 104. At least a server 104 mayinclude any computing device as described herein, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described herein. At least aserver 104 may be housed with, may be incorporated in, or mayincorporate one or more sensors of at least a sensor. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. At least a server 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. At least a server 104 with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting a at least aserver 104 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Atleast a server 104 may include but is not limited to, for example, a atleast a server 104 or cluster of computing devices in a first locationand a second computing device or cluster of computing devices in asecond location. At least a server 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. At least a server 104 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. At least a server 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1, at least a server 104 is configuredto receive training data. Training data, as used herein, is datacontaining correlation that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

With continued reference to FIG. 1, at least a server 104 is configuredto receive a first training set 108 including a plurality of first dataentries, each first data entry of the first training set 108 includingat least an element of physiological state data 112 and at least acorrelated compatible label. At least an element of physiological statedata 112 as used herein, includes any data indicative of a person'sphysiological state. A “compatible element,” as used in this disclosure,is one or more products, ingredients, merchandise, additive, componentcompound, mixture, constituent, element, article, and/or informationcontent that is compatible with a user as described in more detailbelow. A compatible label may include any identifier of any compatibleelement that is compatible with a user. At least an element ofphysiological state data 112 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 112 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 112 may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data 112 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 112 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C(HbA1c)levels. Physiological state data 112 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 112 may includemeasures of estimated glomerular filtration rate (eGFR) Physiologicalstate data 112 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin 1), 25 hydroxy, blood ureanitrogen, creatinine, sodium, potassium, chloride, carbon dioxide, uricacid, albumin, globulin, calcium, phosphorus, alkaline phosphatase,alanine amino transferase, aspartate amino transferase, lactatedehydrogenase (LDH), bilirubin, gamma-glutamyl transferase (GGT), iron,and/or total iron binding capacity (TIBC), or the like. Physiologicalstate data 112 may include antinuclear antibody levels. Physiologicalstate data 112 may include aluminum levels. Physiological state data 112may include arsenic levels. Physiological state data 112 may includelevels of fibrinogen, plasma cystatin C, and/or brain natriureticpeptide.

Continuing to refer to FIG. 1, physiological state data 112 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 112 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 112 may include a measure of waist circumference.Physiological state data 112 may include body mass index (BMI).Physiological state data 112 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 112 may include one or more measures of musclemass. Physiological state data 112 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1, physiological state data 112 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data112 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 112 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

Continuing to refer to FIG. 1, physiological state data 112 may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing module144 as described in this disclosure.

With continued reference to FIG. 1, physiological state data 112 mayinclude one or more evaluations of sensory ability, including measuresof audition, vision, olfaction, gustation, vestibular function and pain.Physiological state data 112 may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 112 may include proteomic data, whichas used herein, is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 112 may include data concerninga microbiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 112 of a person, and/or on compatible labeland/or ameliorative processes as described in further detail below.Physiological state data 112 may include any physiological state data112, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like.

With continuing reference to FIG. 1, physiological state data 112 mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Examples of physiological state data112 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 112 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1, each element of first training set108 includes at least a correlated compatibility label 116. A correlatedcompatibility label, as described herein, is an element of dataidentifying and/or describing any product, ingredient, element,merchandise, additive, component, compound, mixture, constituent,element, article, and/or information content that is compatible with auser as a function of a user's biological extraction. A product mayinclude for example, goods such as but not limited to beauty products,books, electronics, art, food and grocery, health and personal goods,home and garden, appliances, music, office goods, outdoor goods,sporting goods, tools, toys, home improvement, video, digital versatiledisc (DVD), blue-ray, jewelry, musical instruments, computers, cellphones, movies, and the like.

With continued reference to FIG. 1, a correlated compatibility label 116may be associated with one or more elements of physiological state data112. For example, a correlated compatibility label for a product such asshampoo containing parabens may be associated with one or morebiological extractions including Apolipoprotein E Gene 2 (APOE2) andApolipoprotein E Gene 3 (APOE3) and not Apolipoprotein E Gene 4 (APOE4).In yet another non-limiting example, a correlated compatibility labelfor a product containing dextromethorphan may be associated with one ormore elements of physiological data including gene profiles such asextensive metabolizers and ultra-rapid metabolizers and not poormetabolizer. In yet another non-limiting example, a correlatedcompatibility label for literature describing the benefits of melatoninin breast cancer treatment may be associated with one or more elementsof physiological data including positive test results indicating acurrent breast cancer diagnosis, as well as biological extractionsindicating the presence of the breast cancer gene 1 (BRCA1), breastcancer gene 2 (BRCA2), and cyclin-dependent kinase inhibitor 1B gene(CDKN1B). In yet another non-limiting example, a correlated advisorylabel for a product containing classical music may be associated withone or more elements of physiological data including a positivepregnancy test, a positive evaluation for anxiety, and a positiveevaluation for depression. In yet another non-limiting example, acorrelated advisory label for organic makeup may be associated with oneor more elements of physiological data including an elevated thyroidstimulating hormone (TSH), an elevated c-reactive protein (CRP), and anelevated erythrocyte sedimentation rate (ESR).

With continued reference to FIG. 1, correlated compatibility label 116may be stored in any suitable data and/or data type. For instance, andwithout limitation, correlated compatibility label may include textualdata, such as numerical, character, and/or string data. Textual data mayinclude a standardized name and/or code for a disease, disorder, or thelike; codes may include diagnostic codes and/or diagnosis codes, whichmay include without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a compatibilitylabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least an advisory label consistently with this disclosure.

With continued reference to FIG. 1, correlated compatibility label 116may be stored as image data, such as for example an image of aparticular product such as a photograph of a particular sunscreenproduct or an image of a particular book. Image data may be stored invarious forms including for example, joint photographic experts group(JPEG), exchangeable image file format (Exif), tagged image file format(TIFF), graphics interchange format (GIF), portable network graphics(PNG), netpbm format, portable bitmap (PBM), portable any map (PNM),high efficiency image file format (HEIF), still picture interchange fileformat (SPIFF), better portable graphics (BPG), drawn filed, enhancedcompression wavelet (ECW), flexible image transport system (FITS), freelossless image format (FLIF), graphics environment manage (GEM),portable arbitrary map (PAM), personal computer exchange (PCX),progressive graphics file (PGF), gerber formats, 2 dimensional vectorformats, 3 dimensional vector formats, compound formats including bothpixel and vector data such as encapsulated postscript (EPS), portabledocument format (PDF), and stereo formats.

With continued reference to FIG. 1, in each first data element of firsttraining set 108 at least an element of physiological state data 112 iscorrelated with a compatible label 116 where the element ofphysiological data is located in the same data element and/or portion ofdata element as the compatible label; for example, and withoutlimitation, an element of physiological data is correlated with acorrelated element where both element of physiological data andcorrelated element are contained within the same first data element ofthe first training set. As a further example, an element ofphysiological data is correlated with a correlated element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a correlated element where theelement of physiological data and the correlated element share anorigin, such as being data that was collected with regard to a singleperson or the like. In an embodiment, a first datum may be more closelycorrelated with a second datum in the same data element than with athird datum contained in the same data element; for instance, the firstelement and the second element may be closer to each other in an orderedset of data than either is to the third element, the first element andsecond element may be contained in the same subdivision and/or sectionof data while the third element is in a different subdivision and/orsection of data, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and compatible labelthat may exist in first training set 108 and/or first data elementconsistently with this disclosure.

With continued reference to FIG. 1, at least a server 104 may bedesigned and configured to associate at least an element ofphysiological state data 112 with at least a category from a list ofsignificant categories of physiological state data 112. Significantcategories of physiological state data 112 may include labels and/ordescriptors describing types of physiological state data 112 that areidentified as being of high relevance in identifying compatible label.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 112 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or associatedingredients and products that may be compatible with a particulardisease or condition as well as associated ingredients and products thatmay not be compatible with a particular disease or condition. As anon-limiting example, and without limitation, physiological datadescribing disorders associated with heavy metal accumulation includingfor example heart disease, Lyme disease, and Multiple Sclerosis may beuseful in selecting compatible label that include organic ingredientsfree of heavy metals such as lead, mercury, arsenic, cadmium, andchromium. As an additional example, physiological data associated withmental disorders such as anxiety, bipolar disorder, depression, andschizophrenia may be useful in selecting compatible label that includemusic products with calming music such as classical music, smooth jazz,blues, and elevator music. In a further non-limiting example,physiological data describing disorders such as an allergic dermatitisto certain metals such as nickel or lead may be useful in selectingcompatible label that include jewelry that is free of such ingredients.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional categories ofphysiological data that may be used consistently with this disclosure.

Still referring to FIG. 1, at least a server 104 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, at least a server 104 may receive the list ofsignificant categories from at least an expert. In an embodiment, atleast a server 104 may provide a graphical user interface, which mayinclude without limitation a form or other graphical element having dataentry fields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface 120 or thelike may include fields corresponding to compatible label, where expertsmay enter data describing compatible label and/or categories ofcompatible label the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded compatible label, and which may be comprehensive, permittingeach expert to select a compatible label and/or a plurality ofcompatible label the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of compatible label and/or categories of compatible label mayinclude free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Training data may besorted or filtered according to “categories” or “significance scores”whereby training data may be trimmed to categories that are mostsignificant and/or most closely related to a given user's categories.Categories may be used to trim or sort training data according to domainlimitations. Expert input and/or other input of categories including anyof the categories as described herein, can creating training dataentries where categories are a label such as a physiological label orcompatible label, and associations between them may be used to createcorrelations. Categories that are discovered or defined by any processmay become labels such as physiological labels or compatible labels ofthe sorts of things they are related to, in the training data. Data maybe received, a category may be associated with it to create a first kindof label, and a second category may be associated to create a secondkind of label, thereby creating a training data entry. Alternatively oradditionally, fields for entry of compatible label may enable an expertto select and/or enter information describing or linked to a category ofcompatible label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 120 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to compatiblelabel, and/or significant categories of compatible label. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like. Outputsof each machine-learning process may have associated “categories” andmay be sorted or filtered according to “categories” including prior touse as inputs to subsequent machine-learning processes.

With continued reference to FIG. 1, data information describingsignificant categories of physiological data, relationships of suchcategories to compatible label, and/or significant categories ofcompatible label may alternatively or additionally be extracted from oneor more documents using a language processing module 124. Languageprocessing module 124 may include any hardware and/or software module.Language processing module 124 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic: marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 124 may compareextracted words to categories of physiological data recorded by at leasta server 104, one or more compatible label recorded by at least a server104, and/or one or more categories of compatible label recorded by atleast a server 104; such data for comparison may be entered on at leasta server 104 as described above using expert data inputs or the like. Inan embodiment, one or more categories may be enumerated, to find totalcount of mentions in such documents. Alternatively or additionally,language processing module may operate to produce a language processingmodel. Language processing model 124 may include a program automaticallygenerated by at least a server 104 and/or language processing module 124to produce associations between one or more words extracted from atleast a document and detect associations, including without limitationmathematical associations, between such words, and/or associations ofextracted words with categories of physiological data, relationships ofsuch categories to compatible label, and/or categories of compatiblelabel. Associations between language elements, where language elementsinclude for purposes herein extracted words, categories of physiologicaldata, relationships of such categories to compatible label, and/orcategories of compatible label may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of physiological data, a given relationshipof such categories to compatible label, and/or a given category ofcompatible label. As a further example, statistical correlations and/ormathematical associations may include probabilistic formulas orrelationships indicating a positive and/or negative association betweenat least an extracted word and/or a given category of physiologicaldata, a given relationship of such categories to compatible label,and/or a given category of compatible label; positive or negativeindication may include an indication that a given document is or is notindicating a category of physiological data, relationship of suchcategory to compatible label, and/or category of compatible label is oris not significant. For instance, and without limitation, a negativeindication may be determined from a phrase such as “phthalates were notfound to increase the risk of testicular cancer,” whereas a positiveindication may be determined from a phrase such as “phthalates werefound to increase the risk of breast cancer,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryby at least a server 104, or the like.

Still referring to FIG. 1, language processing module 124 and/or atleast a server 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories tocompatible label, and/or a given category of compatible label. There maybe a finite number of category of physiological data, a givenrelationship of such categories to compatible label, and/or a givencategory of compatible label to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 124may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model 124may include generating a vector space, which may be a collection ofvectors, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each vector in an n-dimensional vector spacemay be represented by an n-tuple of numerical values. Each uniqueextracted word and/or language element as described above may berepresented by a vector of the vector space. In an embodiment, eachunique extracted and/or other language element may be represented by adimension of vector space; as a non-limiting example, each element of avector may include a number representing an enumeration ofco-occurrences of the word and/or language element represented by thevector with another word and/or language element. Vectors may benormalized, scaled according to relative frequencies of appearanceand/or file sizes. In an embodiment associating language elements to oneanother as described above may include computing a degree of vectorsimilarity between a vector representing each language element and avector representing another language element; vector similarity may bemeasured according to any norm for proximity and/or similarity of twovectors, including without limitation cosine similarity, which measuresthe similarity of two vectors by evaluating the cosine of the anglebetween the vectors, which can be computed using a dot product of thetwo vectors divided by the lengths of the two vectors. Degree ofsimilarity may include any other geometric measure of distance betweenvectors.

Still referring to FIG. 1, language processing module 124 may use acorpus of documents to generate associations between language elementsin a language processing module and at least a server 104 may then usesuch associations to analyze words extracted from one or more documentsand determine that the one or more documents indicate significance of acategory of physiological data, a given relationship of such categoriesto compatible label, and/or a given category of compatible label. In anembodiment, at least a server 104 may perform this analysis using aselected set of significant documents, such as documents identified byone or more experts as representing good science, good clinicalanalysis, or the like; experts may identify or enter such documents viagraphical user interface, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into at least a server 104. Documents may beentered into at least a server 104 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, at least a server 104 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to compatible label, and/or a given category of compatiblelabel is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to compatible label,and/or category of compatible label may be given an overall significancescore; overall significance score may, for instance, be incremented eachtime an expert submission and/or paper indicates significance asdescribed above. Persons skilled in the art, upon reviewing the entiretyof this disclosure will be aware of other ways in which scores may begenerated using a plurality of entries, including averaging, weightedaveraging, normalization, and the like. Significance scores may beranked; that is, all categories of physiological data, relationships ofsuch categories to compatible label, and/or categories of compatiblelabel may be ranked according significance scores, for instance byranking categories of physiological data, relationships of suchcategories to compatible label, and/or categories of compatible labelhigher according to higher significance scores and lower according tolower significance scores. Categories of physiological data,relationships of such categories to compatible label, and/or categoriesof compatible label may be eliminated from current use if they fail athreshold comparison, which may include a comparison of significancescore to a threshold number, a requirement that significance scorebelong to a given portion of ranking such as a threshold percentile,quartile, or number of top-ranked scores. Significance scores may beused to filter outputs as described in further detail below; forinstance, where a number of outputs are generated and automatedselection of a smaller number of outputs is desired, outputscorresponding to higher significance scores may be identified as moreprobable and/or selected for presentation while other outputscorresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to compatible label,and/or category of compatible label is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to compatible label, and/or category of compatible labelis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to compatible label, and/or category of compatiblelabel is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, at least a server 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to compatible label, and/or categories of compatible labelusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.Unsupervised machine-learning processes that identify associations mayalso create training data sets by creating new categories and creatingdata entries associating them to each other. Unsupervisedmachine-learning processes may identify associations such as by creatingnew categories and allowing experts to identify associations.Unsupervised machine-learning processes may identify associations suchas by obtaining associations from documents linking two newly derivedcategories together.

Continuing to refer to FIG. 1, in an embodiment, at least a server 104may be configured, for instance as part of receiving the first trainingset, to associate at least a correlated first compatible label with atleast a category from a list of significant categories of compatiblelabel. Significant categories of compatible label may be acquired,determined, and/or ranked as described above. As a non-limiting example,compatible label may be organized according to relevance to and/orassociation with a list of significant conditions. A list of significantconditions may include, without limitation, conditions having generallyacknowledged impact on longevity and/or quality of life; this may bedetermined, as a non-limiting example, by a product of relativefrequency of a condition within the population with years of life and/oryears of able-bodied existence lost, on average, as a result of thecondition. A list of conditions may be modified for a given person toreflect a family history of the person; for instance, a person with asignificant family history of a particular condition or set ofconditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult at least a server 104 may modify list of significant categoriesto reflect this difference.

With continued reference to FIG. 1, at least a server 104 is configuredto receive at least a biological extraction from a user. At least abiological extraction, as used herein, includes may include any elementand/or elements of data suitable for use as an element of physiologicalstate data 112. At least a biological extraction may include aphysically extracted sample, which as used herein includes a sampleobtained by removing and analyzing tissue and/or fluid. Physicallyextracted sample may include without limitation a blood sample, a tissuesample, a buccal swab, a mucous sample, a stool sample, a hair sample, afingernail sample, or the like. Physically extracted sample may include,as a non-limiting example, at least a blood sample. As a furthernon-limiting example, at least a biological extraction may include atleast a genetic sample. At least a genetic sample may include a completegenome of a person or any portion thereof. At least a genetic sample mayinclude a DNA sample and/or an RNA sample. At least a biologicalextraction may include an epigenetic sample, a proteomic sample, atissue sample, a biopsy, and/or any other physically extracted sample.At least a biological extraction may include an endocrinal sample. As afurther non-limiting example, the at least a biological extraction mayinclude a signal from at least a sensor configured to detectphysiological data of a user and recording the at least a biologicalextraction as a function of the signal. At least a sensor may includeany medical sensor and/or medical device configured to capture sensordata concerning a patient, including any scanning, radiological and/orimaging device such as without limitation x-ray equipment, computerassisted tomography (CAT) scan equipment, positron emission tomography(PET) scan equipment, any form of magnetic resonance imagery (MRI)equipment, ultrasound equipment, optical scanning equipment such asphoto-plethysmographic equipment, or the like. At least a sensor mayinclude any electromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor may include a temperature sensor. At least a sensor mayinclude any sensor that may be included in a mobile device and/orwearable device, including without limitation a motion sensor such as aninertial measurement unit (IMU), one or more accelerometers, one or moregyroscopes, one or more magnetometers, or the like. At least a wearableand/or mobile device sensor may capture step, gait, and/or othermobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, and/or blood pressure. At least a sensor may be apart of at least a server 104 or may be a separate device incommunication with at least a server 104.

Still referring to FIG. 1, at least a biological extraction may includeany data suitable for use as physiological state data 112 as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least aserver 104 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; at least a server 104 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

Alternatively or additionally, and with continued reference to FIG. 1,at least a biological extraction may include assessment and/orself-assessment data, and/or automated or other assessment results,obtained from a third-party device; third-party device may include,without limitation, a server 104 or other device (not shown) thatperforms automated cognitive, psychological, behavioral, personality, orother assessments. Third-party device may include a device operated byan informed advisor such a functional health care professional includingfor example a functional medicine doctor.

Still referring to FIG. 1, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. At least a server 104 may be configured torecord at least a biological extraction from a user. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various additional examples of at least a biological extractionconsistent with this disclosure.

With continued reference to FIG. 1, at least a server 104 is configuredto receive at least a user activity datum data from a user. A “useractivity datum,” as used in this disclosure, is data describing userbehaviors, user patterns, and/or user actions performed by the user whensearching, purchasing, or the like, for items and/or products containedwithin system 100. User activity data may include web browseractivities, such as user input via a graphical user interface on a webbrowser, such as searching for items, purchasing items online, browsingcatalogues, inventories, and the like. Items and/or products as usedherein, include any physical item of manufacture, a media item, adigital item, and/or a service or other item for purchase or selectioncontained within a networked environment. Items and/or products may becategorized into categories such as beauty, books, business products,camera and photo, electronics, clothing, jewelry, fine art, grocery andgourmet food, health and personal care, home and garden, luggage, travelaccessories, music, musical instruments, office products, shoes,handbags, sports, tools, sports, watches, and the like. Items and/orproducts may include for example shampoo, toothpaste, books, pens, bodywash, cars, computers, tablets, flowers, plants, garden equipment, foodproducts such as chips, candy, cookies, protein bars, and the like. Atleast a user activity datum data may include browsing history ofcompatible elements that user may have created a search query for. Asearch query as used herein, includes any search word and/or phrasesthat a user may enter into a search engine when browsing for aparticular item and/or product. A search query may include a word and/orstring of words describing a particular category of products such as“automobile parts” or “women's handbags.” In yet another non-limitingexample, a search query may include a word and/or string of wordsdescribing a particular product and/or item such as “unscented handlotion” or “organic potato chips.” At least a user activity datum datamay include a term describing a particular product or compatible elementthat a user may be looking for. For example, a user may generate asearch query to find compatible elements relating to electronics thatcontains a query such as “cell phones” or “computers.” At least a useractivity datum data may include a reformulated search query such asdeletion of a term, reformulation of the query, a term swap, a termaddition, a scope change, a refinement of the search query and the like.For example, at least a user activity datum data may include areformulated search query such as an original search query that includes“computer accessory” that is reformulated to include “computer laptopcover.” At least a user activity datum data may include the selection ofan item such as by clicking on a product to learn more about thatparticular item. For example, a user may click on a product such as ashampoo to be taken to a product detail page to learn more about theshampoo such as to read the ingredients contained within the shampoo,the scent of the shampoo, the directions for how to use the shampoo, andthe like. At least a user activity datum data may include an abandonmentquery such as when a user may enter a query but abandon searching forthe query by not selecting a search button or search selection.

With continued reference to FIG. 1, at least a server 104 is configuredto retrieve from a fingerprint database 128 at least a datum of userfingerprint information. Fingerprint database 128 is described in moredetail below in reference to FIG. 7. User fingerprint information asused herein, is any data identifying one or more actions performed by auser in relation to a search query during a search session. For example,user fingerprint information may include data describing several searchqueries that a user entered over the course of a period of time such asover the course of five days. In yet another non-limiting example, userfingerprint information may include a timestamp that includes datadescribing a date and/or time when user entered a particular searchquery. In yet another non-limiting example, timestamp may include datadescribing how long a user searched for products and/or items within aparticular search query or how long a user looked at more informationdescribing a particular product contained within a search query. Forexample, a search query for “flat screen television” may generate twentywebs pages of results and fingerprint information may include datadescribing how long a user spent examining twenty web pages and how longa user spent looking and clicking through particular products listed onthe twenty pages. User fingerprint data 132 may include data describingprevious purchases that a user made such as for example a particularmakeup product that a user purchased seven times in one year or aparticular snack product that a user purchased twice in one week. In anembodiment, user fingerprint information may include fingerprintinformation that is specific to a single user. In an embodiment, userfingerprint information may include fingerprint information that isspecific to a plurality of users. In an embodiment, user fingerprintinformation may include fingerprint information that is specific to aparticular group of users such as users living in the same household orusers with similar diagnostic outputs or biological extraction resultsas described in more detail below in reference to FIG. 2.

With continued reference to FIG. 1, at least a server 104 may beconfigured to retrieve from a behavior database 136 at least a datum ofuser behavior data. Behavior database 136 is described in more detailbelow in reference to FIG. 8. User behavior data, as used herein, is anydata identifying one or more user behaviors in relation to purchasingany item and/or product contained within system 100. User behavior datamay include purchasing history of certain items and/or productscontained within system 100. For example, user behavior data may includean item that a user previously purchased and then returned. Userbehavior data may include an item and/or product that a user purchasedmore than once during a specific period of time. In yet anothernon-limiting example, user behavior data may include an item that a userrepeatedly purchased over a certain period of time such as a toothpastethat a user bought four times in the past two months. User behavior datamay include information describing categories of items and/or productsthat a user purchased over a certain period of time. For example, userbehavior data may include all shoes that a user purchased over the pastthree years or all health and personal care items that user purchasedover the past six months. User behavior data may include informationdescribing same product that a user purchased but manufactured and/orproduced by a separate manufacturer. For example, a user may havepreviously purchased a particular brand of laundry detergent four monthsago and then repurchased a different brand of laundry detergent threemonths ago.

With continued reference to FIG. 1, at least a server 104 is configuredto classify the at least a user activity datum as a function of the atleast a datum of user fingerprint information. Classification as usedherein, includes any element of data identifying and/or describing acategory of a user activity datum. Category may include a class ofsearch queries having particular shared characteristics. At least a useractivity datum and/or a search query may be classified as “broadinquiry” such as when at least a user activity datum includes a requestfor a category of items and/or products such as “garden equipment” or“automobiles.” At least a user activity datum and/or a search query maybe classified as “brand inquiry” such as when at least a user activitydatum includes a request for a specific brand of items and/or productssuch as “Belkin surge protector” or “Apple iPad.” At least a useractivity datum and/or a search query may be classified as “definedinquiry” such as when at least a user activity datum includes a requestfor a defined item and/or product that may be contained within acategory of products and/or items but may not necessarily include arequest for a particular brand or manufacturer. For example, at least auser activity datum and/or a search query may include a request for“smart keyboards” or “eyeshadow.” Classification information such ascategories and/or classification labels may be contained within aclassification database 140. Classification database 140 is described inmore detail below in reference to FIG. 9.

With continued reference to FIG. 1, at least a server 104 may beconfigured to classify the at least a user activity datum function bymatching the at least a datum of user fingerprint information to atleast a datum of previous user activity. Datum of previous useractivity, as used herein includes any action performed by a user inrelation to at least a user activity datum. For example, datum ofprevious user activity may include an item and/or product that a usermay have selected to discover more information about or a specificsearch query that a user repeatedly entered. Datum of previous useractivity may include timestamp information including any of thetimestamp information as described above. For example a datum ofprevious user activity that includes a short search session related to aquery may be indicative of few modifications to a search query whereas along search session related to a query may be indicative of multiplemodifications.

With continued reference to FIG. 1, at least a server 104 may beconfigured to classify the at least a user activity datum as a functionof receiving at least a datum of modified user activity data. Modifieduser activity, as used herein, includes any user activity datum that hasbeen modified after first entry. Modified may include withoutlimitation, a reformulation of the at least a user activity datum, aterm swap, a term addition, a term deletion, an abandonment of the useractivity datum, a refinement of the user activity datum, a scope change,and the like. For example, a modified user activity may include a useractivity datum that includes a search query for a “bicycle” which issearched and then narrowed in scope to include “bicycle for three yearold girl.” In yet another non-limiting example, modified user activitymay include a user activity datum that includes a search query for“dishwasher soap” that is then modified to add a term to read “naturaldishwasher soap.” In yet another non-limiting example, a modified useractivity may include a user activity datum that includes a search queryfor “men's size 11 tennis shoes” that is then modified to delete a termto read “men's size 11 shoes.”

With continued reference to FIG. 1, at least a server 104 is configuredto select at least a compatible element as a function of the at least auser activity datum and the training data. A compatible element mayinclude any of the compatible elements as described above. A compatibleelement may include for example one or more products, ingredients,merchandise, additive, component compound, mixture, constituent,element, article, and/or information content that is compatible with auser. A compatible element may include a particular brand of product, aparticular ingredient contained within a product, a particular categoryof products, a particular category of ingredients, a particular productline, a particular ingredient line. For example, a compatible elementmay include a shampoo that contains ingredients that won't cause user'sseborrheic eczema to flare up. In yet another non-limiting example, acompatible element may include a list of music artists that won't worsena user's intermittent explosive disorder. In yet another non-limitingexample, a compatible element may include a list of makeup free of moldfor a user with mold toxicity. In yet another non-limiting example,compatible element may contain a list of cleaning products free ofgluten for a user with Celiac Disease. Compatibility includes one ormore products, ingredients, merchandise, additive, component compound,mixture, constituent, element, article, and/or informational contentthat is capable of use and/or consumption by a user without an adverseeffect. An adverse effect may include any negative effect on longevity,health condition, mortality, and/or quality of life of a user. Forexample, a user with dermatitis herpetiformis who uses hand soapcontaining gluten may experience an adverse response such as ablistering rash on body parts exposed to gluten containing hand soap. Inyet another non-limiting example, a user with small intestinal bacterialovergrowth (SIBO) who consumes kombucha rich in microorganisms mayexperience an adverse response such as bloating, gas, and diarrhea. Inyet another non-limiting example, a user with breast cancersusceptibility gene (BRCA 1 or BRCA 2) who uses personal care itemscontaining phthalates may experience an adverse effect such as a greaterrisk of developing breast cancer. In an embodiment, a compatible elementcontaining a plurality of products and/or ingredients may be ranked inorder of compatibility. For example, a compatible element containingthree shampoos that may be suitable for use by a user with a lactoseallergy may be listed in order of compatibility from most compatibledown to least compatible. In such an instance, products and/oringredients may be ranked such as for example most compatible if aproduct was manufactured in a certified lactose free facility whereas aproduct may be ranked least compatible if it was manufactured in afacility that doesn't use lactose as an ingredient but is not acertified lactose free facility. Rankings and order of compatibility maybe customized around a user's individual needs whereby one product for auser with celiac disease that is certified gluten free may be highlyranked for one user while that same product may be least compatible fora user with a corn allergy because it is not manufactured in a certifiedcorn free facility. Compatible elements may be contained within acompatible element database 144 as described in more detail below inreference to FIG. 15.

With continued reference to FIG. 1, at least a server 104 may beconfigured to select at least a compatible element using amachine-learning algorithm and the training set. System 100 may includea first label learner operating at least a server. First label learnermay be designed and configured to select at least a compatible elementusing a first machine-learning algorithm and the first training datarelating physiological data to compatible label. At least a firstmachine-learning model 152 may include one or more models that determinea mathematical relationship between physiological data and compatiblelabel. Such models may include without limitation model developed usinglinear regression models. Linear regression models may include ordinaryleast squares regression, which aims to minimize the square of thedifference between predicted outcomes and actual outcomes according toan appropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 152 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, first label learner may generatecompatibility output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained ng first training set;the trained network may then be used to apply detected relationshipsbetween elements of physiological state data and compatible label.

With continued reference to FIG. 1, at least a server 104 may select atleast a compatible element as a function of a compatible elementcategory. Compatible element category, as used herein is an element ofdata which identifies a compatible element having particular sharedcharacteristics. Shared characteristics may include traits, and/orqualities that identify a compatible element as being uses for aparticular purpose and/or being used for a particular condition. Atleast a compatible element category may include a descriptionidentifying a compatible element as being used for a particular purpose.For example, a compatible element such as a television may be labeledwith a compatible element category such as “electronic” while acompatible element such as body wash may be labeled with a compatibleelement category such as “health and personal care.” In yet anothernon-limiting example, a compatible element such as a food product may belabeled with a compatible element category such as “grocery & gourmetfood” while a compatible element such as hiking boots may be labeledwith a compatible element category such as “outdoors.” In an embodiment,a compatible element may contain a plurality of compatible elementcategories, for example a toaster oven may be labeled with a firstcompatible element category such as “electronic” and with a secondcompatible element category such as “kitchenware.”

With continued reference to FIG. 1, at least a server 104 may beconfigured to select at least a compatible element by retrieving atleast a compatible element similarity index value from a database andselecting at least a compatible element as a function of the compatibleelement similarity index value. Compatible element similarity indexvalue as used herein, is a value assigned to a compatible elementindicating a degree of similarity between a first compatible element anda second compatible element. In an embodiment, compatible element indexscores may be stored in a database or datastore as described below inmore detail in reference to FIG. 15. In an embodiment, a compatibleelement index may be calculated based on correlations between past userpurchase history, past overall purchase history, and similarity ofproducts and/or product ingredients. In an embodiment, compatibleelement index may be ranked whereby a high compatible element indexbetween any two compatible elements may indicate that for any twocompatible elements a large percentage of users who browsed, selected,and/or purchased a first compatible element then browsed, selected,and/or purchased a second compatible element. A low compatible elementindex between any two compatible elements may indicate that for any twocompatible elements a small percentage of users who browsed, selected,and/or purchased a first compatible element then browsed, selected,and/or purchased a second compatible element. In an embodiment,compatible element index may be utilized to generate a compatibleelement index list that may be generated for a given compatible elementby selecting N other compatible elements that have the highestcompatible element index number and including those compatible elementson the compatible element index list. Compatible element index isdescribed below in more detail in reference to FIG. 15.

With continued reference to FIG. 1, at least a server 104 may transmitthe at least a compatible element to a user client device 156. A userclient device 156 may include, without limitation, a display incommunication with at least a server 104; display may include anydisplay as described herein. A user client device 156 may include anadditional computing device, such as a mobile device, laptop, desktopcomputer, or the like; as a non-limiting example, the user client device156 may be a computer and/or workstation operated by a medicalprofessional. Output may be displayed on at least a user client device156 using an output graphical user interface, as described in moredetail below. Transmission to a user client device 156 may include anyof the transmission methodologies as described herein.

With continued reference to FIG. 1, at least a server 104 may include adiagnostic engine 160 operating on at least a server 104, wherein thediagnostic engine 160 may be configured to receive at least a biologicalextraction from a user and generate at least a diagnostic output as afunction of the training data and the at least a biological extraction.At least a diagnostic output may include at least a prognostic label andat least an ameliorative process label. At least a server 104,diagnostic engine 160, and/or one or more modules operating thereon maybe designed and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, at least aserver 104 and/or diagnostic engine 160 may be configured to perform asingle step or sequence repeatedly until a desired or commanded outcomeis achieved; repetition of a step or a sequence of steps may beperformed iteratively and/or recursively using outputs of previousrepetitions as inputs to subsequent repetitions, aggregating inputsand/or outputs of repetitions to produce an aggregate result, reductionor decrement of one or more variables such as global variables, and/ordivision of a larger processing task into a set of iteratively addressedsmaller processing tasks. At least a server 104 and/or diagnostic engine160 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing. Diagnosticengine 160 may be configured to record at least a biological extractionfrom a user and generate a diagnostic output based on the at least abiological extraction. Diagnostic engine 160 is described in more detailbelow in reference to FIG. 2.

With continued reference to FIG. 1, system 100 may include a parsingmodule operating on at least a server 104. Parsing module may includeany suitable hardware or software module. Parsing module may beconfigured to extract at least an element from at least a user activitydatum wherein the at least an element may include at least a compatibleelement neutralizer and retrieve at least a datum of user fingerprintdata 132 as a function of the at least an element. Compatible elementneutralizer, as used herein, includes any process that may improve anyphysical condition identifiable in a diagnostic output. Compatibleelement neutralizer may include medications, supplements, nutrients,herbal remedies, exercise programs, medical procedures, physicaltherapies, psychological therapies and the like. In an embodiment,compatible element neutralizer may include compatible elements that maybe contraindicated during the course of treatment with a particularcompatible element neutralizer. For example, a compatible elementneutralizer may include a specific medication designed to treat a user'snail fungus or compatible element neutralizer may include a particularsupplement utilized to balance out a user's symptoms of estrogendominance. In yet another non-limiting example, compatible elementneutralizer may include treatment with a medication that containscontraindicated therapies, foods, and supplements during the course oftreatment with the medication. Compatible element neutralizer may beutilized to select at least a compatible element such as when a certainmedication, supplement, and/or medical procedure may be associated witha compatible element. For example, a compatible element neutralizer suchas a statin medication that is utilized to reduce total cholesterollevels may be utilized to select at least a compatible element such asubiquinol. In yet another non-limiting example, a compatible elementneutralizer such as supplementation with zinc may be utilized to selectat least a compatible element such as copper. In yet anothernon-limiting example, a compatible element neutralizer may be utilizedto not select at least a compatible element. For example, a compatibleelement neutralizer such as doxycycline may be utilized to not select atleast a compatible element containing products that may interfere withabsorption of doxycycline such as magnesium, aluminum, calcium, iron,and laxatives. In an embodiment, parsing module is configured to extractat least an element from the at least a user activity datum wherein theat least an element may include at least a compatible elementneutralizer and retrieve at least a datum of user fingerprint data as afunction of the at least an element. For instance and withoutlimitation, compatible element neutralizer may be contained within useractivity datum which may be utilized to retrieve at least a datum ofuser fingerprint data that may be relevant during the course oftreatment with compatible element neutralizer. For example, a user mayhave a compatible element neutralizer such as treatment with a statinmedication for high cholesterol whereby consumption of grapefruit andgrapefruit containing food products are contraindicated. In such aninstance, parsing module may extract compatible element neutralizer fromthe at least a user activity datum and retrieve at least a datum of userfingerprint data relevant to user's grapefruit restriction duringtreatment with statin compatible element neutralizer. Such userfingerprint data may include user's browsing and/or purchasing historyfor compatible elements that do not include grapefruit such as apurchase of cranberry juice or orange juice. As a non-limiting example,parsing module may extract at least an element from at least a useractivity datum such as a particular item or product that may becontained within at least a user activity datum. Parsing module may beconfigured to extract one or more words. One or more words may includewithout limitation, strings of one or more characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,images such as emojis, whitespace, and other symbols. Textual data maybe parsed into segments, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term segments as used hereinrefers to any smaller, individual groupings of text from a larger sourceof text; segments may be broken up by word, pair of words, sentence, orother delimitation. These segments may in turn be parsed in variousways. Textual data may be parsed into words or sequences of words, whichmay be considered words as well. Textual data may be parsed into“n-grams”, where all sequences of n consecutive characters areconsidered. Any or all possible sequences of segments or words may bestored as “chains”, for example for use as a Markov chain or HiddenMarkov Model.

With continued reference to FIG. 1, at least a server 104 may beconfigured to store at least a user activity datum in the fingerprintdatabase 128. User activity datum may be stored as any suitable dataand/or data type. For instance, and without limitation, user activitydatum may include textual data such as numerical, character, and/orstring data. Textual data may include a standardized name and/or code.In yet another non-limiting example, user activity datum may includeimage data including for example an image of a user's browsing historyor a photograph of a user activity datum. Images may be stored as any ofthe various forms as described above. Storing at least a user activitydatum in the fingerprint database 128 may create a feedback mechanismthat allows for fingerprint database 128 to be continuously updated withuser activity datums as user activity datums are generated.

Referring now to FIG. 2, an exemplary embodiment of diagnostic engine160 is illustrated. In an embodiment, diagnostic engine 160 may beconfigured to record at least a biological extraction from a user,generate a diagnostic output based on the training data and the at leasta biological extraction, and select at least a compatible element as afunction of the at least a diagnostic output. At least a biologicalextraction may include any of the biological extractions as describedabove in reference to FIG. 1. In an embodiment, diagnostic engine 160may generate a diagnostic output based on the at least a biologicalextraction using training data and a machine-learning model. Trainingdata may include any of the training data as described above inreference to FIG. 1. In an embodiment, diagnostic engine 160 may receivea second training set 200 including a plurality of first data entries,each first data entry of the second training set 200 including at leastan element of physiological state data 204 and at least a correlatedfirst prognostic label 208. Physiological state data 204 may include anyof the physiological state data 112 as described above in reference toFIG. 1.

Continuing to refer to FIG. 2, each element of second training set 200includes at least a first prognostic label 208. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or heathy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 204 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 2, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 2, in each first data element of secondtraining set 200, at least a first prognostic label 208 of the dataelement is correlated with at least an element of physiological statedata 204 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe second training set 200. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 108 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 2, diagnostic engine 160may be designed and configured to associate at least an element ofphysiological state data 204 with at least a category from a list ofsignificant categories of physiological state data 204. Significantcategories of physiological state data 204 may include labels and/ordescriptors describing types of physiological state data 204 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 204 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 2, diagnostic engine 160 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 160 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 160 and/or a user device connected to diagnosticengine 160 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

With continued reference to FIG. 2, data information describingsignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or significant categories ofprognostic labels may alternatively or additionally be extracted fromone or more documents using a language processing module 216. Languageprocessing module 216 may include any hardware and/or software module.Language processing module 216 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 2, language processing module 216 may compareextracted words to categories of physiological data recorded atdiagnostic engine 160, one or more prognostic labels recorded atdiagnostic engine 160, and/or one or more categories of prognosticlabels recorded at diagnostic engine 160; such data for comparison maybe entered on diagnostic engine 160 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 216 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine160 and/or language processing module 216 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 160, or the like.

Still referring to FIG. 2, language processing module 216 and/ordiagnostic engine 160 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs has used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 216may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

With continued reference to FIG. 2, at least a server 104 and/ordiagnostic engine 160 may be configured to receive a second training setincluding a plurality of second data entries. Each second data entry ofthe second training set may include at least a compatible label 116 andat least a correlated compatible category label. Correlation may includeany correlation suitable of at least an element of physiological statedata 112 and at least a correlated compatible label 116 as describedabove. As used herein, a compatible category label is a classifier whichidentifies compatible products and/or ingredients having particularshared characteristics. Shared characteristics may include traits and/orqualities that identify a product and/or ingredient as being used for aparticular purpose and/or suitable for a particular condition. Forexample, products free of gluten and dairy may contain a compatiblecategory label as indicating products free of gluten and dairy. In yetanother non-limiting example, a product such as organic toothpaste thatdoesn't contain any preservatives or heavy metals and is sourced onlyfrom plants may contain a compatible category label as indicating beingsuitable for use by those most at risk for heavy metal toxicityincluding persons with mercury dental fillings, smokers, and users withchronic autoimmune conditions. Training data may be sorted or filteredaccording to “categories” or “significance scores” such as by trimmingtraining data to categories that are most significant and/or mostclosely related to a given user's categories. In an embodiment, trainingdata may be sorted according to domain limitations. Expert input orother input of “categories” may create training data entries where“categories” are a kind of label and associations between them are usedto create correlations. “Categories” discovered or defined by anyprocess may become labels of the sorts of things they are related to, inthe training data. Training data may be received, a category may beassociated with it to create a first kind of label, and a secondcategory may be associated with it to create a second kind of label,thereby creating a training data entry. Unsupervised machine learningidentification of associations may also create training data by creatingnew categories and creating data entries associating them to each other.Unsupervised machine-learning identification of associations may createtraining data by making new categories and allowing experts to identifyassociations or making new categories and obtaining associations fromdocuments linking two newly derived categories together. Outputs of eachmachine-learning process may have associated “categories” and may besorted or filtered according to “categories” including prior to use asinputs to subsequent processes.

With continued reference to FIG. 2, at least a server 104 and/ordiagnostic engine 160 may be configured to receive component elements oftraining sets and utilize components to generate machine-learning modelsto select at least a compatible element. Components may include any ofthe data sets described in first training set, second training set, andthird training set. For example, at least a server may receivecomponents and relate elements between first prognostic label 208 andcompatible label or compatible category labels using machine-learningmodels as described herein. In yet another non-limiting example, atleast a server 104 and/or diagnostic engine 160 may relate elementsbetween ameliorative process label 228 and compatible label orameliorative process label 228 and compatible category labels. In yetanother non-limiting example, at least a server 104 and/or diagnosticengine 160 may relate elements between diagnostic outputs and compatiblelabel or diagnostic output and compatible category labels.

Continuing to refer to FIG. 2, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 2, language processing module 216 may use acorpus of documents to generate associations between language elementsin a language processing module 216, and diagnostic engine 160 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 160 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into diagnostic engine 160. Documents may beentered into diagnostic engine 160 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, diagnostic engine 160 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 2, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of biological extraction, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 2, diagnostic engine 160 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 2, in an embodiment, diagnostic engine 160may be configured, for instance as part of receiving the second trainingset 200, to associate at least correlated first prognostic label 208with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 160 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 2, diagnostic engine 160 is designed andconfigured to receive a third training set 220 including a plurality ofsecond data entries. Each second data entry of the third training set220 includes at least a second prognostic label 224; at least a secondprognostic label 224 may include any label suitable for use as at leasta first prognostic label 208 as described above. Each second data entryof the third training set 220 includes at least an ameliorative processlabel 228 correlated with the at least a second prognostic label 224,where correlation may include any correlation suitable for correlationof at least a first prognostic label 208 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 228 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Continuing to refer to FIG. 2, in an embodiment diagnostic engine 160may be configured, for instance as part of receiving third training set220, to associate the at least second prognostic label 224 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 208.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set108 according to a first process as described above and for prognosticlabels in third training set 220 according to a second process asdescribed above.

Still referring to FIG. 2, diagnostic engine 160 may be configured, forinstance as part of receiving third training set 220, to associate atleast a correlated ameliorative process label 228 with at least acategory from a list of significant categories of ameliorative processlabels 228. In an embodiment, diagnostic engine 160 and/or a user deviceconnected to diagnostic engine 160 may provide a second graphical userinterface 232 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 216 or the like as described above.

In an embodiment, and still referring to FIG. 2, diagnostic engine 160may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 160 may be configured, for instanceas part of receiving third training set 220, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 228; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 228, and/orefficacy of ameliorative process labels 228 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 216 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 2, diagnostic engine 160 may beconfigured, for instance as part of receiving third training set 220, toreceiving at least a second data entry of the plurality of second dataentries from at least an expert. This may be performed, withoutlimitation using second graphical user interface 148 as described above.

With continued reference to FIG. 2, diagnostic engine 160 may include aprognostic label learner 236 operating on the diagnostic engine 160, theprognostic label learner 236 designed and configured to generate the atleast a prognostic output as a function of the second training set 200and the at least a biological extraction. Prognostic label learner 236may include any hardware and/or software module. Prognostic labellearner 236 is designed and configured to generate outputs using machinelearning processes. A machine learning process is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed by acomputing device/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 2, prognostic label learner 236 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 240 relating physiological statedata 204 to prognostic labels using the second training set 200 andgenerating the at least a prognostic output using the firstmachine-learning model 240; at least a first machine-learning model 240may include one or more models that determine a mathematicalrelationship between physiological state data 204 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithm used togenerate first machine-learning model 240 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 2, prognostic label learner 236 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using second trainingset 200; the trained network may then be used to apply detectedrelationships between elements of physiological state data 204 andprognostic labels.

Referring now to FIG. 3, data incorporated in first training set may beincorporated in one or more databases. As a non-limiting example, one orelements of physiological data may be stored in and/or retrieved from abiological extraction database 300. A biological extraction database 300may include any data structure for ordered storage and retrieval ofdata, which may be implemented as a hardware or software module. Abiological extraction database 300 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. A biological extractiondatabase 300 may include a plurality of data entries and/or recordscorresponding to elements of physiological data as described above. Dataentries and/or records may describe, without limitation, data concerningparticular biological extractions that have been collected; entries maydescribe reasons for collection of samples, such as without limitationone or more conditions being tested for, which may be listed withrelated compatible label. Data entries may include compatible labeland/or other descriptive entries describing results of evaluation ofpast biological extractions, including diagnoses that were associatedwith such samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by diagnostic engine 160 128 in previousiterations of methods, with or without validation of correctness bymedical professionals. Data entries in a biological extraction database300 may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a biological extraction and/or a person from whom abiological extraction was extracted or received with one or morecohorts, including demographic groupings such as ethnicity, sex, age,income, geographical region, or the like, one or more common diagnosesor physiological attributes shared with other persons having biologicalextractions reflected in other data entries, or the like. Additionalelements of information may include one or more categories ofphysiological data as described above. Additional elements ofinformation may include descriptions of particular methods used toobtain biological extractions, such as without limitation physicalextraction of blood samples or the like, capture of data with one ormore sensors, and/or any other information concerning provenance and/orhistory of data acquisition. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a biological extraction database 300 may reflectcategories, cohorts, and/or populations of data consistently with thisdisclosure.

With continued reference to FIG. 3, at least a server 104 and/or anotherdevice in communication with at least a server 104 may populate one ormore fields in biological extraction database 300 using expertinformation, which may be extracted or retrieved from an expertknowledge database 304. An expert knowledge database 304 may include anydata structure and/or data store suitable for use as a biologicalextraction database 300 as described above. Expert knowledge database304 may include data entries reflecting one or more expert submissionsof data such as may have been submitted according to any processdescribed above in reference to FIGS. 1-2 including without limitationby using graphical user interface 120 and/or second graphical userinterface 148. Expert knowledge database may include one or more fieldsgenerated by language processing module, such as without limitationfields extracted from one or more documents as described above. Forinstance, and without limitation, one or more categories ofphysiological data and/or related compatible label and/or categories ofcompatible label associated with an element of physiological state data112 as described above may be stored in generalized from in an expertknowledge database 304 and linked to, entered in, or associated withentries in a biological extraction database 300. Documents may be storedand/or retrieved by at least a server 104 and/or language processingmodule in and/or from a document database 308; document database 308 mayinclude any data structure and/or data store suitable for use asbiological extraction database 300 as described above. Documents indocument database 308 may be linked to and/or retrieved using documentidentifiers such as URI and/or URL data, citation data, or the like;persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which documents may beindexed and retrieved according to citation, subject matter, author,date, or the like as consistent with this disclosure.

With continued reference to FIG. 3, a compatible label database 312,which may be implemented in any manner suitable for implementation ofbiological extraction database 300, may be used to store compatiblelabels used by at least a server 104, including any compatible labelcorrelated with elements of physiological data in first training set 108as described above; compatible label may be linked to or refer toentries in biological extraction database 300 to which compatible labelcorrespond. Linking may be performed by reference to historical dataconcerning biological extractions, such as diagnoses, prognoses, and/orother medical conclusions derived from biological extractions in thepast; alternatively or additionally, a relationship between a compatiblelabel and a data entry in biological extraction database 300 may bedetermined by reference to a record in an expert knowledge database 304linking a given compatible label to a given category of biologicalextraction as described above. Entries in compatible label database 312may be associated with one or more categories of compatible label asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 304.

With continued reference to FIG. 3, first training set 108 may bepopulated by retrieval of one or more records from biological extractiondatabase 300 and/or compatible label database 312; in an embodiment,entries retrieved from biological extraction database 300 and/orcompatible label database 312 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a first training set 108 including data belonging to agiven cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom at least a server 104 classifies biological extractionsto compatible label as set forth in further detail below. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which records may be retrieved frombiological extraction database 300 and/or compatible label database togenerate a first training set 108 to reflect individualized group datapertaining to a person of interest in operation of system and/or method,including without limitation a person with regard to whom at least abiological extraction is being evaluated as described in further detailbelow. At least a server 104 may alternatively or additionally receive afirst training set 108 and store one or more entries in biologicalextraction database 300 and/or compatible label database 312 asextracted from elements of first training set.

Referring now to FIG. 4, one or more database tables in biologicalextraction database 300 may include, as a non-limiting example, ncompatible link table 400. Compatible link table 400 may be a tablerelating biological extraction data as described above to compatiblelabel; for instance, where an expert has entered data relating acompatible label to a category of biological extraction data and/or toan element of biological extraction data via first graphical userinterface as described above, one or more rows recording such an entrymay be inserted in compatible link table 400. Alternatively oradditionally, linking of compatible label to biological extraction datamay be performed entirely in compatible label database as describedbelow.

With continued reference to FIG. 4, biological extraction database 300may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 300 may include a fluid sample table 404 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 300 may include asensor data table 408, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, biological extraction database 300 may include agenetic sample table 412, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, biological extractiondatabase 300 may include a medical report table 416, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, biological extraction database 300 mayinclude a tissue sample table 420, which may record biologicalextractions obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in biologicalextraction database 300 consistently with this disclosure.

Referring now to FIG. 5, an exemplary embodiment of an expert knowledgedatabase 304 is illustrated. Expert knowledge database 304 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 304 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 304 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in expertknowledge database 304 may include, as a non-limiting example, an expertcompatible table 500. Expert compatible table 500 may be a tablerelating biological extraction data as described above to compatiblelabel; for instance, where an expert has entered data relating acompatible label to a category of biological extraction data and/or toan element of biological extraction data via graphical user interface120 as described above, one or more rows recording such an entry may beinserted in expert compatible table 500. In an embodiment, a formsprocessing module 504 may sort data entered in a submission viagraphical user interface 120 by, for instance, sorting data from entriesin the graphical user interface 120 to related categories of data; forinstance, data entered in an entry relating in the graphical userinterface 120 to a compatible label may be sorted into variables and/ordata structures for storage of compatible label, while data entered inan entry relating to a category of physiological data and/or an elementthereof may be sorted into variables and/or data structures for thestorage of, respectively, categories of physiological data or elementsof physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 124 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module 124 mayindicate that entry should be treated as relating to a new label; thismay be determined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 508, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 124. Data may be extracted from expert papers512, which may include without limitation publications in medical and/orscientific journals, by language processing module 124 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert compatible table 500 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of compatible label such as books, beauty,electronics, health and personal care, home and garden, outdoors, (notshown), to name a few non-limiting examples presented for illustrativepurposes only.

With continued reference to FIG. 5, one or more database tables inexpert knowledge database 304 may include, an expert user input table516, expert data populating such tables may be provided, withoutlimitation, using any process described above, including entry of datafrom graphical user interface 120 via forms processing module 504 and/orlanguage processing module 124, processing of textual submissions 508,or processing of expert papers 512. For instance, and withoutlimitation, an expert user input table 516 may list one or categories ofuser input processes, and/or links of such one or more user inputsprocesses to compatible labels, as provided by experts according to anymethod of processing and/or entering expert data as described above. Asa further example an expert compatible category table 520 may list oneor more expert compatible categories based on compatible labels and/orbiological extractions, including for example a compatible categorytable for skin care suitable for use by users who have diabetes, acompatible category table for clothing suitable for use by users whohave diabetes, and a compatible category table for sporting goodssuitable for use by users who have rheumatoid arthritis as provided byexperts according to any method of processing and/or entering expertdata as described above. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database304.

Referring now to FIG. 6, an exemplary embodiment of compatible labeldatabase 312 is illustrated. Compatible label database 312 may, as anon-limiting example, organize data stored in the compatible labeldatabase 312 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofcompatible label database 312 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 6, one or more database tables in compatiblelabel database 312 may include, as a non-limiting example, an extractiondata table 600. Extraction data table 600 may be a table listing sampledata, along with, for instance, one or more linking columns to link suchdata to other information stored in compatible label database 312. In anembodiment, extraction data 604 may be acquired, for instance frombiological extraction database 300, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 608, which may perform unit conversions. Datastandardization module 608 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 124 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 6, compatible label database 312 may includean extraction label table 612; extraction label table 612 may listcompatible label 116 received with and/or extracted from biologicalextractions, for instance as received in the form of extraction text616. A language processing module 124 may compare textual information soreceived to compatible label 116 and/or from new compatible label 116according to any suitable process as described above. Extractionadvisory link table 620 may combine extractions with compatible label116, as acquired from extraction label table and/or expert knowledgedatabase 304; combination may be performed by listing together in rowsor by relating indices or common columns of two or more tables to eachother. Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in expert knowledge database 304 consistently with thisdisclosure.

Referring now to FIG. 7, an exemplary embodiment of fingerprint database128 is illustrated, which may be implemented in any manner suitable forimplementation of biological extraction database 300. Fingerprintdatabase 128 may include user fingerprint information including any ofthe fingerprint data 132 as described above in reference to FIG. 1. Oneor more database tables in fingerprint database 128 may include, withoutlimitation a search query table 700; search query table 700 may includeinformation describing one or more search queries and/or search queryactions that a user entered within system 100. One or more searchqueries may include any data and/or information describing any previoussearch queries that a user entered. For example, search query table 700may include information describing a particular product and/or item thata user generated a search query for such as a food product such ascoffee or a cleaning product such as dish soap. Search query actions mayinclude any action performed by a user in relation to a query. Searchquery actions may include for example, a reformulation of a searchquery, a term swap, a term addition, a term addition, an abandonment ofthe search query, a refinement of the search query, a scope change ofthe search query and the like. For example, search query table 700 mayinclude data describing a user who refines a search query from “kitchenutensil” to “metal spatula.” One or more database tables in fingerprintdatabase 128 may include, without limitation a timestamp table 704;timestamp table 704 may include information describing time informationpertaining to any particular search query. Timestamp table 704 mayinclude time information such as the date, time, and/or geolocation of auser when entering or creating a search query. Timestamp table 704 mayinclude time information such as how long a user spent creating a searchquery, how long a user spent reformatting a search query, and/or howlong a user spent looking at search results from a search query.Timestamp table 704 may include time information such as how long a userbrowsed through particular items and/or products contained within system100, as well as how long a user spent reading through a particularproduct and/or item detail page. One or more database tables infingerprint database 128 may include, without limitation a browsingtable 708; browsing table 708 may include information describingbrowsing patterns of a particular user. Browsing patterns may includewhat products and/or items a user may select to look at, categories ofproducts and/or items that a user may look at, as well as productsand/or items a user may select to examine from a list generated after asearch query request. For example, browsing table 708 may includeinformation such as a list of products and/or items that a user lookedat during a particular search session. One or more database tables infingerprint database 128 may include, without limitation a productdetail table 712; product detail table 712 may include informationdescribing any product and/or item details that a user may have selectedto viewed. For example, product detail table 712 may include informationdescribing a shampoo that a user selected to view more detailedinformation about such as ingredients or scent. In yet anothernon-limiting example, product detail table 712 may include informationdescribing a towel that a user selected to view detailed information tofind out if the cotton used to produce the towel was grown organicallyand without the use of artificial dyes. One or more database tables infingerprint database 128 may include, without limitation a productselection table 716; product selection table 716 may include informationdescribing one or more items and/or products that a user may select forpurchase but never actually purchase. For example, product selectiontable 716 may include information describing one or more products and/oritems that a user may place in an electronic shopping cart to purchaselater. One or more database tables in fingerprint database 128 mayinclude, without limitation a miscellaneous table 720; miscellaneoustable 720 may include any other information pertaining to a previoussearch query, and/or user activity datum.

Referring now to FIG. 8, an exemplary embodiment of behavior database136 is illustrated, which may be implemented in any manner suitable forimplementation of biological extraction database 300. Behavior database136 may include user behavior data, including any of the user behaviordata as described above in reference to FIG. 1. One or more databasetables in behavior database 136 may include product return table 800;product return table 800 may include any information describing anyproduct and/or item that a user may have returned after purchase. Forexample, product return table 800 may include information describing abody lotion that a user returned after purchase and received money backor a cell phone that a user returned and received money back. Productreturn table 800 may include information such as an item and/or productthat a user may have exchanged for a different product and/or item. Forexample, product return table 800 may include information such as anitem and/or product that a user exchanged for credit or exchanged for adifferent product or item made by the same brand. One or more databasetables in behavior database 136 may include product repeat table 804;product repeat table 804 may include any items and/or products that auser may have purchased on more than one occasion. For example, productrepeat table 804 may include information describing a product such as abody wash that a user purchased three times or a protein bar that a userpurchased seven times. One or more database tables in behavior database136 may include timestamp table 808; timestamp table 808 may includetime information such as the date, time, and/or geolocation of a productand/or item that a user purchased. For example, timestamp table 808 mayinclude information describing the time that a user purchased a steelwater container or the geo-location of a user who purchased an item suchas a fragrance free bar of soap. One or more database tables in behaviordatabase 136 may include product category table; product category tablemay include information describing one or more categories of productsand/or items that a user previously purchased. Product categories mayinclude any of the product categories as described herein and below inreference to FIG. 12. For example, product category table 812 mayinclude information describing electronic purchases of a user or beautypurchases of a user. One or more database tables in behavior database136 may include product brand table 816; product brand table 816 mayinclude information describing particular brands of products and/oritems that a user previously purchased. For example, product brand table816 may include information describing a particular brand of toiletpaper that a user purchased that was free of dyes or a particular brandof iced tea that a user purchased that was sold in a glass container.One or more database tables in behavior database 136 may includemiscellaneous table 820; miscellaneous table 820 may include any otherinformation pertaining to a user behavior data.

Referring now to FIG. 9, an exemplary embodiment of classificationdatabase 140 is illustrated, which may be implemented in any mannersuitable for implementation of biological extraction database 300.Classification database 140 may include any information describingclassifications of search queries and/or user activity data. One or moredatabase tables contained within classification database 140 may includebroad inquiry table 900; broad inquiry table 900 may include informationdescribing search queries and/or user activity classified as broadinquiries. Broad inquires may include any search queries and/or useractivity that do not specify a particular brand or manufacturer and/ormay contain a request for a category of product and/or item. Forexample, a search query containing “tennis racquet” may be categorizedas a broad inquiry. In yet another non-limiting example, a user activitydatum that includes a request for a toy may be categorized as a broadinquiry. One or more database tables contained within classificationdatabase 140 may include brand inquiry table 904; brand inquiry table904 may include information describing search queries and/or useractivity classified as brand inquiries. Brand inquiries may include anysearch queries and/or user activity that specify a particular brand itemand/or product or a particular manufacturer of a particular item and/orproduct. For example, a search query containing “Dell Laptop” may becategorized as a brand inquiry. In yet another non-limiting example, auser activity datum that includes a request for “BareMinerals makeup”may be categorized as a brand inquiry. One or more database tablescontained within classification database 140 may include defined inquirytable 908; defined inquiry table 908 may include information describingsearch queries and/or user activity classified as defined inquires.Defined inquires may include any search queries and/or user activitythat specify a request for a defined item and/or product that may becontained within a category of products and/or items but may notnecessarily include a request for a particular brand or manufacturer.For example, a search query containing “cast iron skillet” may becategorized as a defined inquiry. In yet another non-limiting example, auser activity datum that includes a request for “cleaning product freeof phthalate” may be categorized as a defined inquiry. One or moredatabase tables contained within classification database 140 may includemiscellaneous inquiry table 912; miscellaneous inquiry table 912 mayinclude any inquiry that may not be categorized according to one of thecategories as described herein.

Referring now to FIG. 10, an exemplary embodiment of compatible elementdatabase 144 is illustrated, which may be implemented in any mannersuitable for implementation of biological extraction database 300.Compatible element database 144 may, as a non-limiting example, organizedata stored in compatible element database 144 according to one or moredatabase tables. For instance, a common column between two tables ofcompatible element database 144 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

With continued reference to FIG. 10, compatible element database 144 mayinclude a biological extraction link table 1000; biological extractionlink table 1000 may link biological extraction data to compatibleelement data, using any suitable method for linking data in two or moretables as described above. Compatible element database 144 may includebeauty table 1004; beauty table 1004 may include beauty productscompatible with a given biological extraction. For example, beauty table1004 may include information describing brands of makeup that may befree of parabens and heavy metals for a user with heavy metal toxicity.Compatible element database 144 may include literature table 1008;literature table 1008 may include information describing literaturecompatible with a given biological extraction. For example, literaturetable 1008 may include a list of books, magazines, brochures, articles,pamphlets, and/or other reading materials that may be suitable for auser with a given biological extraction. For example, literature table1008 may include a motivational book for a user with depression or anarticle describing different spiritual practices for a user with cancer.Compatible element database 144 may include sports table 1012; sportstable 1012 may include information describing sporting equipment thatmay be compatible for a user with a given biological extraction. Forexample, sports table 1012 may include information such as golf clubs,golf balls, and croquet rackets for a user with kidney disease or a userwho has only one kidney and has prohibitions on playing contact sports.In yet another non-limiting example, sports table 1012 may includeinformation such as tennis rackets, tennis balls, and jogging sneakersfor a user with cardiovascular disease. Compatible element database 144may include health and personal care table 1016; health and personalcare table 1016 may include information describing health and personalcare products that may be compatible for a user with a given biologicalextraction. For example, health and personal care table 1016 may includeinformation such as possible shampoos, conditioners, body wash, toothpaste and the like that do not contain synthetic estrogens or estrogenmimicking compounds for a user with CYP19A1 gene mutation. Compatibleelement database 144 may include grocery and gourmet food table 1020;grocery and gourmet food table 1020 may include information describinggrocery items and foods that may be compatible for a user with a givenbiological extraction. For example, grocery and gourmet food table 1020may include information such as food products such as crackers, cookies,and snacks that do not contain dairy for a user with a mutation in MCM6gene responsible for lactase enzyme production. Compatible elementdatabase 144 may include a single table and/or a plurality of tables;plurality of tables may include tables for particular categories ofcompatible elements such as but not limited to books, beauty,electronics, health, musical instruments, toys and games, jewelry, homeand garden, outdoors, (not shown), to name a few non-limiting examplespresented for illustrative purposes only.

Referring now to FIG. 11, an exemplary embodiment of first label learner148 is illustrated. Machine-learning algorithms used by first labellearner 148 may include supervised machine-learning algorithms, whichmay, as a non-limiting example be executed using a supervised learningmodule 1100 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data 112 as inputs, compatible labels 116 asoutputs, and a scoring function representing a desired form ofrelationship to be detected between elements of physiological data 112and compatible labels 116; scoring function may, for instance, seek tomaximize the probability that a given element of physiological data 112and/or combination of elements of physiological data is associated witha given compatible label 116 and/or combination of compatible labels 116to minimize the probability that a given element of physiological data112 and/or combination of elements of physiological data is notassociated with a given compatible label 116 and/or combination ofcompatible labels 116. Scoring function may be expressed as a riskfunction representing an “expected loss” of an algorithm relating inputsto outputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationsbetween elements of physiological data 112 and compatible labels 116. Inan embodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of compatible labels 116, and/or are specifiedas linked to a medical specialty and/or field of medicine covering aparticular set of compatible label 116. As a non-limiting example, aparticular set of blood test biomarkers may be typically used toeliminate certain compatible elements such as for example positive BRACA1 or BRACA 2 gene mutations and a need to eliminate certain known breastcancer causing chemicals such as bisphenol A and phthalates, and asupervised machine-learning process may be performed to relate thoseblood test biomarkers to the correlated compatible products; in anembodiment, domain restrictions of supervised machine-learningprocedures may improve accuracy of resulting models by ignoringartifacts in training data. Domain restrictions may be suggested byexperts and/or deduced from known purposes for particular evaluationsand/or known tests used to evaluate compatible label 116. Additionalsupervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between physiological data 112 and compatiblelabel 116.

With continued reference to FIG. 11, machine-learning algorithms mayinclude unsupervised processes; unsupervised processes may, as anon-limiting example, be executed by an unsupervised learning module1104 executing on at least a server 104 and/or on another computingdevice in communication with at least a server 104, which may includeany hardware or software module. An unsupervised machine-learningprocess, as used herein, is a process that derives inferences indatasets without regard to labels; as a result, an unsupervisedmachine-learning process may be free to discover any structure,relationship, and/or correlation provided in the data. For instance, andwithout limitation, first label learner 148 and/or at least a server 104may perform an unsupervised machine learning process on first trainingset, which may cluster data of first training set 108 according todetected relationships between elements of the first training set,including without limitation correlations of elements of physiologicaldata 112 to each other and correlations of compatible label 116 to eachother; such relations may then be combined with supervised machinelearning results to add new criteria for first label learner 148 toapply in relating diagnostic output to compatible label 116. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first element of user physiological data acquired in ablood test correlates closely with a second element of userphysiological data, where the first element has been linked viasupervised learning processes to a given compatible label 116, but thesecond has not; for instance, the second element may not have beendefined as an input for the supervised learning process, or may pertainto a domain outside of a domain limitation for the supervised learningprocess. Continuing the example a close correlation between firstelement of user physiological data and second element of userphysiological data may indicate that the second element is also a goodpredictor for the compatible label 116; second element may be includedin a new supervised process to derive a relationship or may be used as asynonym or proxy for the first physiological data element by first labellearner 148.

Still referring to FIG. 11, at least a server 104 and/or first labellearner 148 may detect further significant categories of userphysiological data, relationships of such categories to compatiblelabels 116, and/or categories of compatible labels 116 usingmachine-learning processes, including without limitation unsupervisedmachine-learning processes as described above; such newly identifiedcategories, as well as categories entered by experts in free-form fieldsas described above, may be added to pre-populated lists of categories,lists used to identify language elements for language processing module124, and/or lists used to identify and/or score categories detected indocuments, as described above. In an embodiment, as additional data isadded to system 100, first label learner 148 and/or at least a server104 may continuously or iteratively perform unsupervisedmachine-learning processes to detect relationships between differentelements of the added and/or overall data; in an embodiment, this mayenable system 100 to use detected relationships to discover newcorrelations between known biomarkers, and/or compatible labels 116 andone or more elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes as described in further detailbelow to identify relationships between, e.g., particular clusters ofgenetic alleles and particular compatible labels 116 and/or suitablecompatible labels 116. Use of unsupervised learning may greatly enhancethe accuracy and detail with which system may detect compatible label116.

With continued reference to FIG. 11, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofcompatible label 116; as illustrative examples, cohort could include allpeople having a certain level or range of levels of blood triglycerides,all people diagnosed with anxiety, all people with a SRD5A2 genemutation, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of a multiplicity of ways inwhich cohorts and/or other sets of data may be defined and/or limitedfor a particular unsupervised learning process.

Still referring to FIG. 11, first label learner 148 may alternatively oradditionally be designed and configured to generate at least acompatible output 1108 by executing a lazy learning process as afunction of the first training set 108 and/or at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 1112 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata compatible label 116 associated with a user physiological test sample,using first training set. As a non-limiting example, an initialheuristic may include a ranking of compatible label 116 according torelation to a test type of at least a physiological test sample, one ormore categories of physiological data identified in test type of atleast a physiological test sample, and/or one or more values detected inat least a physiological test sample; ranking may include, withoutlimitation, ranking according to significance scores of associationsbetween elements of physiological data 112 and compatible label 116, forinstance as calculated as described above. Heuristic may includeselecting some number of highest-ranking associations and/or compatiblelabel 116. First label learner 148 may alternatively or additionallyimplement any suitable “lazy learning” algorithm, including withoutlimitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate compatible outputs 1108 asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Referring now to FIG. 12, an exemplary embodiment of product categories1200 is illustrated. Product categories may include products and/oritems having shared characteristics. In an embodiment, productcategories may be displayed to a user such as through graphical userinterface 120. In an embodiment, a user may select a product categorysuch as through graphical user interface 120 from a drop-down menu andsubmit a search query directed towards a particular product category.Product categories may include but are not limited to automotive, babyproducts, beauty, books, business products, camera and photo, cellphones, clothing, collectible coins, electronics, jewelry, fine art,grocery and gourmet food, handmade products, historical collectibles,home and garden, health and personal care, luggage and travelaccessories, music, office products, outdoors, shoes, handbags, sports,toys, and watches. In an embodiment, product categories may be organizedinto sub-categories. For example and without limitation, beauty productsmay be separated into further sub-categories to include makeup, skincare, hair care, fragrance, foot hand and nail care, tools andaccessories, shave and hair removal, personal care, and oral care. In anembodiment, sub-categories may be further broken down into furthersub-categories that share certain attributes including but not limitedto natural, organic, cruelty free, paraben free, hypoallergenic,unscented, alcohol free and the like. In yet another non-limitingexample, a product category such as electronics may be further organizedinto sub-categories that include computers and accessories, tv andvideo, cell phones and accessories, home audio, headphones, officeelectronics, office supplies, smart home, musical instruments, and videogames. In such an instance, a sub-category such as computers andaccessories may be further broken down into further sub-categoriesincluding desktops, laptops, tablets, monitors, computer accessories,computer components, drives and storage, networking, office supplies,printers, and gaming.

Referring now to FIG. 13, an exemplary embodiment of a graphical userinterface 1300 is illustrated. For example and illustrative purposesonly, graphical user interface 120 may include a screen whereby a usermay enter a search query to search for products and/or items containedwithin system 100. In an embodiment, and for illustrative purposes onlyGUI 120 may include a search query field 1304 whereby a user can enter asearch query. In an embodiment, search query field 1304 may include adrop-down menu of choices based on previous user entries. Textual inputsentered into search query field 1304 may be utilized to create useractivity datums. Previous search queries including previous entries intosearch query field 1304 may be stored in fingerprint database 128. GUI120 may include product category field 1308 whereby a user may enterand/or select a product category. Product category may include any ofthe product categories as described herein, including any of the productcategories described above in reference to FIG. 12. GUI 120 may includeproduct sub-category field 1312, which a user may enter and/or select asub-category related to product category field 1308. In an embodiment,product sub-category may be auto-generated into a list based on enteredand/or selected product category field 1308. GUI 120 may display firstcompatible element 1316. GUI 120 may display second compatible element1320, and third compatible element 1324. GUI 120 may display nthcompatible element 1328 such as for example when there are multiplecompatible elements. In an embodiment, compatible elements may be rankedaccording to a particular ranking scheme such as by percentage ofcompatibly, with highest percentage compatible elements ranked at thetop and descending compatible elements listed in descending order. GUI120 may include product categories that a user may click on to findcompatible elements contained within categories. Categories may includefor illustrative purposes only automotive 1332, baby products 1336,beauty 1340, books 1344, business products 1348, camera and photo 1352,and cell phones 1356. Categories that a user may click upon may includeany of the categories described herein, including any of the categoriesas listed above in reference to FIG. 12.

Referring now to FIG. 14, an exemplary embodiment of user database 1400is illustrated, which may be implemented in any manner suitable forimplementation of biological extraction database 300. User database 1400may include user information and/or user preferences that may beutilized by at least a server 104 when selecting at least a firstcompatible element. In an embodiment, first learner 148 may utilize datastored within user database 1400 to generate user specific trainingsets. One or more database tables in user database 1400 may include,without limitation, a user demographic table 1404; user demographictable 1404 may include information describing demographic informationpertaining to user. For example, demographic table 1404 may includeinformation describing user's name, address, phone number, race, gender,marital status, education level, employment information, total income,and the like. One or more database tables in user database 1400 mayinclude, without limitation, a user biological extraction table 1408;user biological extraction table 1408 may include information and/ordata stored about one or more biological extractions from a user. Forexample, user biological extraction table 1408 may include informationdescribing results from a user's blood test or results from a salivatest. In an embodiment, user biological extraction table 1408 may beorganized and/or categorized such as in chronological order, and/or byextraction type. One or more database tables in user database 1400 mayinclude, without limitation, a user history table 1412; user historytable 1412 may include information regarding history of user'sinteractions with system 100. For example, user history table 1412 mayinclude data describing previous purchases a user made or previousproducts and/or items user browsed. In an embodiment, user history table1412 may include any of the user history information contained withinfingerprint database 128. In yet another non-limiting example, userhistory table 1412 may include information such as products and/oringredients that a user placed into an electronic shopping cart orelectronic shopping basket and possibly saved for later or later cameback and purchased. One or more database tables in user database 1400may include, without limitation, user preference table 1416; userpreference table 1416 may include information describing a user'spreference for particular products, ingredients, and/or brands ofproducts or ingredients. For example, user preference table 1416 mayinclude information describing user's preference for a particular brandof shampoo user routinely purchases or user's preference for aparticular company's line of cleaning products. In an embodiment, userpreference table 1416 may include information regarding a user'spreference for a particular product or ingredient based on a ranking orreview that user may have attributed to a particular product oringredient. Information contained within user database 1400 may beobtained from user client device 156 156 and/or through informationprovided through graphical user interface 120.

Referring now to FIG. 15, an exemplary embodiment of compatible elementsimilarity index value database 1500 is illustrated, which may beimplemented in any manner suitable for implementation of biologicalextraction database 300. Similarity index value database 1500 mayinclude information describing similarity index values for differentproducts and/or items. Similarity index value database 1500 may beconsulted by at least a server 104 when selecting at least a compatibleelement. Similarity index is a value assigned to a compatible elementindicating a degree of similarity between a first compatible element anda second compatible element. Similarity may include a degree of likenessbetween a first compatible element and a second compatible element.Similarity index value may contain information allowing for at least aserver 104 to select one or more compatible elements that are similar inresponse to a particular search query. For example, similarity indexvalue may be utilized by at least a server 104 to select multipleproducts that may be suggested to a user with a search query such as“wagon for young children” or a search query such as “hair conditionerfree of ammonia.” Similarity index value may also be utilized to suggestother products and/or items such as when a product and/or item may notbe in stock, may be on backorder, may be too expensive for a user andthe like. Similarity index value database 1500 may be organized intocategories of compatible elements. Categories may include categories ofproducts and/or items. In an embodiment, categories may includecategories describing functionality and/or utility of differentcompatible elements.

With continued reference to FIG. 15, one or more database tables incompatible element similarity index value database 1500 may include,without limitation beauty table 1504; beauty table 1504 may includesimilarity index values for all compatible elements categorized asbeauty. For example, beauty table 1104 may include similarity indexvalues for compatible elements such as for example, skin serum, retinolcream, face wash, makeup brushes, shaving cream, face masks, face spray,eye cream, and the like. In an embodiment, compatible elements containedwithin beauty table 1504 may be further categorized into sub-categoriessuch as tools, eye products, face products, body products, hairproducts, female beauty products, male beauty products, and the like.One or more database tables in compatible element similarity index valuedatabase 1500 may include, without limitation books table 1508; bookstable 1508 may include similarity index values for all compatibleelements categorized as books. For example, books table 1508 may includesimilarity index values for compatible elements such as biographies &memoirs, children's books, history books, law books, medical books,mystery books, romance books, religious books, science fiction books,self-help books, sports & outdoor books, teen & young adult books,travel books and the like. In an embodiment, books table 1508 may befurther categorized into sub-categories such as award winners, topsellers, new releases, bargain books, top twenty lists, celebrity picks,local authors, and the like. One or more database tables in compatibleelement similarity index value database 1500 may include, withoutlimitation electronics table 1512; electronics table 1512 may includesimilarity index values for all compatible elements categorized aselectronics. For example, electronics table 1512 may include similarityindex values for compatible elements such as computers, printers,headphones, televisions, projectors, cell phones, tablets, video games,and the like. In an embodiment, electronics table 1512 may be furthercategorized into sub-categories such as devices, smart home devices,television, camera, computers, accessories, car electronics, portableelectronics, software, video games, and the like. One or more databasetables in compatible element similarity index value database 1500 mayinclude, without limitation grocery and gourmet foods table 1516;grocery and gourmet foods table 1516 may include similarity index valuesfor all compatible elements categorized as grocery and gourmet foods.For example, grocery and gourmet foods table 1516 may include similarityindex values for compatible elements such as foods, beverages, foodstorage products, food replacements and the like. In an embodiment,groceries and gourmet foods table 1516 may be further categorized intosub-categories such as baby food, alcoholic beverages, beverages, breadsand bakery, breakfast foods, candy, chocolate, dairy, cheese, plants,meal kits, frozen, meat, seafood, meat substitutes, pantries staples,and the like. One or more database tables in compatible elementsimilarity index value database 1500 may include, without limitationhome and garden table 1520; home and garden table 1520 may includesimilarity index value for compatible elements such as plants, seeds,garden equipment, outdoor equipment, and the like. In an embodiment,home and garden table 1520 may be further categorized intosub-categories such as plants, seeds, bulbs, patio furniture, patioseating, canopies, gazebos, planters, outdoor lighting, lawn mowers,outdoor power tools, garden sculptures, grills, and gardening tools. Oneor more database tables in compatible element similarity index valuedatabase 1500 may include, without limitation music table 1524; musictable 1524 may include similarity index values for compatible elementssuch as specific songs, artists, albums, and the like. In an embodiment,music table 1524 may be further categorized into sub-categories such asChristian contemporary music, country, rap, jazz, rock, pop, classical,Broadway vocalists, R & B, vocal pop, and the like. Informationcontained within compatible element similarity index value database 1500may be obtained from user client device 156 156 and/or throughinformation provided through graphical user interface 120. Compatibleelement similarity index value database 1500 may include a single tableand/or a plurality of tables; plurality of tables may include tables forparticular categories of compatible elements such as health and personalcare, outdoors, automotive, baby products, camera and photo, cell phoneand accessories, entertainment, art, design, appliances, musicalinstruments, office products, personal computers, sports, sportcollectibles, tools and home (not shown), to name a few non-limitingexamples presented for illustrative purposes only.

Referring now to FIG. 16, an exemplary embodiment of a method 1600 ofusing artificial intelligence to analyze user activity data isillustrated. At step 1605 at least a server receives training data.Receiving training data may include receiving a first training set 108including a plurality of first data entries, each first data entry ofthe plurality of first data entries including at least an element ofphysiological state data 112 and at least a correlated compatible label116. Element of physiological state data 112 may include any of thephysiological state data 112 as described above in reference to FIGS.1-16. Correlated compatible label 116 may include any of the correlatedcompatible label 116 as described above in reference to FIGS. 1-16. Inan embodiment, receiving first training set 108 may include associatingthe at least an element of physiological state data 112 with at least acategory from a list of significant categories of physiological statedata 112. In an embodiment, significant categories may be received froman expert as described above in reference to FIG. 1. Receiving trainingdata may be performed utilizing any of the methodologies as describedabove in reference to FIGS. 1-16.

With continued reference to FIG. 16, at step 1610 at least a serverreceives from a user at least a biological extraction and at least auser activity datum. Biological extraction may include any of thebiological extractions as described above in reference to FIG. 1013. Forinstance and without limitation, receiving at least a biologicalextraction may including receiving a datum of information describing aparticular genetic mutation or a particular diagnosed condition of auser. For example, at least a server 104 may receive at least abiological extraction describing a user's MCM6 mutation impairing auser's ability to produce the lactase enzyme. In an embodiment, at leasta biological extraction may be stored by at least a server 104 such asin a memory component. In an embodiment, at least a server 104 mayrecord at least a biological extraction from a user. Recording at leasta biological extraction may be performed utilizing any of themethodologies as described above in reference to FIGS. 1-16. In anembodiment, at least a biological extraction received from a user may beutilized by at least a server to generate diagnostic output as afunction of the training data and the at least a biological extraction.In an embodiment, diagnostic output may be generated by a diagnosticengine 160 operating on at least a server. In such an instance, at leasta compatible element may be selected as a function of the at least adiagnostic output.

With continued reference to FIG. 16, at least a server receives at leasta user activity datum. User activity datum may include any of the useractivity datums as described above in reference to FIGS. 1-16. In anembodiment, at least a user activity datum may include receiving a usersearch query such as text that a user searched for. For example, atleast a user activity datum may include a user search query such as“wide brimmed hat” or “fragrance free body lotion.” In an embodiment, atleast a user activity datum may include timestamp information such ashow long a user searched for a particular product and/or item. In anembodiment, at least a user activity datum may include any actionsperformed by a user in relation to a query such as for example, areformulation of a query, a term swap, a term addition, a term deletion,an abandonment of a query, a scope change, and the like. At least a useractivity datum may be received using any methodologies described herein,including any network methodologies as described below in reference toFIG. 18.

With continued reference to FIG. 16, at step 1615 at least a serverretrieves from a fingerprint database 128 at least a datum of userfingerprint information. Fingerprint database 128 may include any of thefingerprint databases 128 as described above in reference to FIG. 1 andFIG. 7. Fingerprint database 128 may include stored fingerprint data132. Stored fingerprint data 132 may include any of the fingerprint data132 as described above in reference to FIG. 1 and FIG. 7. Fingerprintdata 132 may include for example, data identifying one or more actionsperformed by a user in relation to a search query during a searchsession. Fingerprint data 132 may include timestamps including any ofthe timestamps as described above in reference to FIGS. 1-16. Forexample, fingerprint data 132 may include information describing howlong a user examined a particular product information page or how long auser spent reformatting a search query. In an embodiment, at least aserver may include a parsing module that may extract at least an elementfrom the at least a user activity datum wherein the at least an elementmay include at least a compatible element neutralizer and retrieve atleast a datum of user fingerprint data 132 as a function of the at leastan element. Compatible element neutralizer may include any of thecompatible element neutralizers as described above in reference toFIG. 1. In an embodiment, compatible element neutralizer may be utilizedto select and/or not select at least a compatible element. For example,a compatible element neutralizer such as a treatment with a bloodthinning medication such as warfarin may be utilized to not select atleast a compatible element such as a multi-vitamin that contains VitaminK. In yet another non-limiting example, a compatible element neutralizersuch as treatment with warfarin may be utilized to select at least acompatible element such as a multi-vitamin that does not contain VitaminK. Parsing module is configured to extract at least an element where theelement may include at least a compatible element neutralizer from atleast a user activity datum and retrieve at least a datum of userfingerprint data as a function of the at least an element. For example,compatible element neutralizer containing a course of treatment of aspecific length in duration may be utilized to retrieve at least a userfingerprint datum pertinent to the duration of treatment with thecompatible element neutralizer. Such information may be utilized toselect and/or recommend compatible elements that a user may utilize, andwhich may be pertinent to a user's browsing history and selection duringwhich time a compatible element neutralizer is imposed on a user. In anembodiment, element may include certain textual inputs, words, string ofwords, characters, and/or numerical values that may be utilized toretrieve at least a datum of user fingerprint data 132. For example,element may include a certain time period to retrieve, or a particularproduct or product category to retrieve. In an embodiment, at least anelement may include certain information identifying a user that may beutilized by at least a server to retrieve at least a datum of userfingerprint data 132. For instance and without limitation, user may beidentified by a cryptographically secure numerical code that may becontained within at least an element and matched to at least a datum ofuser fingerprint data 132 to confirm user identity.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

With continued reference to FIG. 16, at least a server may receive atleast a user activity datum and retrieve from a behavior database 136 atleast a datum of user behavior data. At least a user activity datum mayinclude any of the datums of user activity as described above inreference to FIGS. 1-16. Behavior database 136 may include any of thebehavior databases 136 as described above in reference to FIG. 1 andFIG. 8. In an embodiment, behavior database 136 may include behaviordata describing purchasing history and trends of a user. For example,behavior database 136 may include data describing products and/or itemsthat a user may have returned. In yet another non-limiting example,behavior database 136 may include data describing repeated purchases ofproducts and/or items of a user. For example, data describing a user'spurchase of the same hand soap three times in the past six weeks may bestored within behavior database 136. Behavior database 136 may includedata describing particular brands that a user may have purchased in thepast. User behavior data including timestamp data may also be retrievedfrom behavior database 136.

With continued reference to FIG. 16, at step 1620 at least a serverclassifies at least a user activity datum as a function of the at leasta datum of user fingerprint information. Classification of at least adatum user activity may include any of the classification methodologiesas described above in reference to FIG. 1 and FIG. 9. In an embodiment,classification categories may be stored in classification database 140as described above in more detail in reference to FIG. 9. In anembodiment, at least a user activity datum may be classified as a “broadinquiry” such as when at least a user activity datum includes anonspecific request for a product and/or item such as a productcategory. For example, at least a user activity datum that includes asearch query for “electronics” may be classified as broad. In anembodiment, at least a user activity datum may be classified as “brandinquiry” such as when at least a user activity datum contains a requestfor a particular brand product and/or item such as “Louis Vuittonwallet” or “Apple TV.” In an embodiment, at least a user activity datummay be classified by matching at least a datum of user fingerprintinformation to at least a datum of previous user activity. In such aninstance, classifications may be customized to any particular user anduser's search query histories. For example, at least a user activitydatum that is categorized as “broad” for one user may be categorized asa defined inquiry for another user based on previous user fingerprintinformation and previous categorization of user activity datums. In anembodiment, at least a user activity datum may be classified as afunction of receiving at least a datum of modified user activity.Modified user activity may include any of the modified user activity asdescribed above in reference to FIGS. 1-16. For example, at least a useractivity datum that is subsequently modified such as reformulating asearch query or swapping a term may be reclassified as a function of amodified user activity. For example, at least a user activity datum mayinitially be classified as “broad” and may subsequently be modified toinclude a brand name product leading to the modified user activity datumto be subsequently classified as “brand.”

With continued reference to FIG. 16, at step 1625 at least a serverselects at least a compatible element as a function of the at least auser activity datum and the training data. Selecting at least acompatible element may include using a machine-learning algorithm andthe training set. Machine-learning algorithm may include any of themachine-learning algorithms as described above in reference to FIGS.1-16. Training data may include any of the training data as describedabove in reference to FIGS. 1-16. Selecting at least a compatibleelement may include selecting at least a compatible element as afunction of a compatible element category. Compatible element categorymay include any of the compatible element categories as described abovein reference to FIGS. 1-16. In an embodiment, compatible elementcategory may include any of the compatible element categories asdescribed above in reference to FIG. 12. Selecting at least a compatibleelement may include retrieving at least a compatible element similarityindex value from a database and selecting at least a compatible elementas a function of the compatible element similarity index value.Compatible element similarity index value may include any of thecompatible element similarity index values as described above inreference to FIG. 1. In an embodiment, compatible element similarityindex value may be stored in compatible element similarity index valuedatabase as described above in more detail in reference to FIG. 10.

With continued reference to FIG. 16, at least a server is furtherconfigured to store at least a user activity datum in fingerprintdatabase 128. Storing at least a user activity datum in fingerprintdatabase 128 may provide a feedback mechanism whereby subsequent useractivity datums are subsequently stored after at least a server receivesat least a user activity datum. In an embodiment, training data that isspecific to a particular user may be stored in fingerprint database 128.In an embodiment, information that has been updated within fingerprintdatabase 128 may be utilized to update training sets. In an embodiment,training data may be continuously updated with subsequently receivedbiological extractions and compatibility labels.

With continued reference to FIG. 16, at step 1630 at least a servertransmits the at least a compatible element to a user client device 156.User client device 156 may include any of the user client devices 156 asdescribed herein. Transmission may occur using any of the transmissionmethodologies as described herein, including transmission methodologiesas described below in reference to FIG. 18.

Referring now to FIG. 17, an exemplary embodiment of a method 1700 ofgenerating compatible elements is illustrated. At step 1705, computingdevice is designed and configured for receiving from a user, at least abiological extraction and at least a user activity datum. Receiving atleast a user activity datum may include receiving user input via agraphical user interface; this may be implemented, without limitation,as described above in FIGS. 1-16.

Continuing in reference to FIG. 17, biological extraction may includeany physiological state data 112, as described above. Biologicalextraction data may include data collected, analyzed by, and/or derivedfrom a wearable device. A “wearable device,” as used in this disclosure,is a device on the person of a user that collects biological extractiondata about the user, where “on the person” indicates that the device isportable and is either worn on the user, inside the user, in contactwith user, or in close proximity to the user. Biological extraction mayinclude data generated, collected, and/or transmitted by a wearabledevice and may include wearables worn by the by user such as anaccelerometer, pedometer, gyroscope, fitness trackers, force monitors,motion sensors; wearables in contact with a user's skin such as inelectrocardiography (ECG), electrooculography (EOG), bioimpedance, bloodpressure and heart rate monitoring, oxygenation data, biosensors;wearables that may be placed inside and/or within a user, for instance,beneath the skin, such as pacemakers, capsule cameras, biosensors,endoscopes, and the like; and/or devices that may be adapted to beplaced outside of the user but aimed at collecting data pertaining tothe user, such as audio-visual capture, social media platform data,magnetic resonance imaging (MRI), X-ray imaging, facial recognition, andthe like. Wearable devices may be any devices capably and useful inacquiring, measuring, and/or transmitting biometrics—body measurementsand calculates related to human characteristics. Biometric data mayinclude any data that is useful in biometrically identifying a user,including fingerprints, retina scans, genetic material data, physicalappearance, voice recognition, or any other data useful in identifyingan individual or useful in determining and/or ordering compatibleelements.

Continuing in reference to FIG. 17, a “graphical user interface,” asused in this disclosure, is any form of a user interface that allows auser to interface with an electronic device through graphical icons anddisplays, audio indicators, text-based interface, typed command labels,text navigation, and the like, wherein the interface is configured toprovide information to the user and accept input from the user. Agraphical user interface may be an interface that a user interacts withto search for items via a web-based browser, online GUI, or the like. Agraphical user interface may be used by system 100 to collect and/oranalyze user input, including for instance and without limitation, userbiological extraction and/or user activity data.

Still referring to FIG. 17, at step 1710, computing device is configuredfor determining a current user location. The current user location mayinclude an alimentary provider, wherein the current user locationincludes an alimentary provider, and identifying the plurality ofcompatible elements may include identifying the compatible elements as afunction of the alimentary provider; this may be implemented, withoutlimitation, as described above in FIGS. 1-16.

Continuing in reference to FIG. 17, a “user location,” as used in thisdisclosure, is a physical location of the user. A current user locationmay include data describing addresses, global positioning system (GPS)coordinates, latitude and/or longitude, and/or any other data describinga user's location in real-time, for instance a user location where thecurrent is located at the time of using system. A user location mayinclude information regarding to nearby businesses, entities, and/orestablishments. A user location may include a business, entities, and/orestablishment a user is currently inside. A current user location may bedetermined and/or retrieved by using any mapping application, software,and/or application, such as a mobile application, web-based application,or the like.

Continuing in reference to FIG. 17, an “alimentary provider,” as used inthis disclosure, is an entity that may prepare and/or generate analimentary element for user, such as a restaurant, fast food chain,grocery store, food truck, and the like. An “alimentary element,” asused in this disclosure, is a compatible element that a user may ingestand/or consume, such as a meal, food item, beverage, nutritionsupplement, or the like. An alimentary provider may be simply referredto for the purposes of this disclosure as a “provider”. An alimentaryprovider may include a stationary provider, a traveling provider (suchas a delivery provider), and/or a provider with a specific location. Analimentary element provider may include an individual and/or a business.A current user location may include an alimentary provider location. Analimentary provider location may include a menu, item list, or the like,that may be associated with provider and may be retrieved by system 100for identifying compatible elements, filtering compatible elements,selecting compatible elements, and/or providing compatible elements, asdescribed herein.

Continuing in reference to FIG. 17, at step 1715, computing device isconfigured for generating a diagnostic output as a function of thebiological extraction, wherein the diagnostic output includes acondition of the user. Generating the diagnostic output may includetraining a diagnostic machine-learning model with training data, whereintraining data includes receiving a training set including a plurality offirst data entries, each first data entry of the plurality of first dataentries includes at least an element of biological extraction datacorrelated to a user condition, and generating a diagnostic output as afunction of the trained diagnostic machine-learning model and thebiological extraction; this may be implemented, without limitation, asdescribed above in FIGS. 1-16.

Continuing in reference to FIG. 17, a “diagnostic output,” as used inthis disclosure, is a machine-learning model, function, heuristic, orthe like, that contains data about the condition of the user. Adiagnostic output may contain data describing a relationship of acompatible element on the user condition. A diagnostic output mayinclude data regarding which items, ingredients, and the like, may haveadverse effects on a user condition. A “user condition,” as used in thisdisclosure, is an indication of a user's current biological,physiological, and/or mental state, including any symptoms, diseases,injuries, adverse effects, or the like. As described above, userconditions may include, without limitation, conditions having generallyacknowledged impact on longevity and/or quality of life; this may bedetermined, as a non-limiting example, by a product of relativefrequency of a condition within the population with years of life and/oryears of able-bodied existence lost, on average, as a result of thecondition. A list of conditions may include family history; forinstance, a person with a family history of a particular condition orset of conditions, a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult at least a server 104 may modify list of significant categoriesto reflect this difference. User condition may include allergies, foodintolerances, or the like, which may affect identifying, filtering,and/or selecting compatible elements, for instance, as selected from arestaurant. User condition may include philosophical, religious,lifestyle, and/or social conditions that may affect the selection ofcompatible elements based on differentiation between products includingdesignations such as ‘Kosher’, ‘vegan’, ‘cruelty-free’, ‘fair trade’,and the like.

Continuing in reference to FIG. 17, diagnostic machine-learning modelmay be any model performed and/or executed by computing device using amachine-learning algorithm, program, process, or the like, as describedabove. Computing device may accept an input of training data to trainthe diagnostic machine-learning model to generate an output that ismodel that may describe a diagnostic output. The diagnostic output mayinclude a determination, prediction, or the like, that describes how theuser condition may be affected by a compatible element, including thecurrent user condition. For instance and without limitation, diagnosticmachine-learning model may output a diagnostic output that describes theeffect that a variety of alimentary elements may have on the usercondition. In such an example, some alimentary elements may greatlyimprove user condition as the user may be deficient in a nutrient,improving the user condition. Likewise, it may be the case that thediagnostic output includes data indicating an adverse effect to the usercondition from tree nuts as the medical history of the user indicates apotential allergy. Diagnostic machine-learning model training data mayinclude biological extraction data, such as medical history, familyhistory, genetic and/or epigenetic data, microbiome data, and the like.The training data may include wearable device data that includestracking user exercise, fitness, sleep patterns, and the like. Trainingdata may be collected and/or input via a graphical user interface by theuser for, instance and without limitation, via a questionnaire,telemedicine session, or the like.

Continuing in reference to FIG. 17, at step 1720, computing device isconfigured for retrieving, from a fingerprint database 128, at least adatum of user fingerprint data 132. Retrieving the at least a datum ofuser fingerprint data may include generating a classifier using aclassification machine-learning process, wherein the classifier relatesat least a user activity datum classified to compatible element orderingbehaviors, and retrieving the at least a datum of user fingerprint dataas a function of the at least a user activity datum and the classifier;this may be implemented, without limitation, as described above in FIGS.1-16.

Continuing in reference to FIG. 17, user fingerprint data 132, asdescribed above, may include user activity data such as web-browsinghistory, order history, and the like. User fingerprint data 132 mayinclude spending behaviors, patterns, and the like. User fingerprintdata 132 may be retrieved from fingerprint database 128 by use of aclassifier. A “classifier,” as used in this disclosure, is configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, as described in further detail below.A classifier may represent a body of data that is a series of biometricdata from a plurality of user activity data associated with spendingbehavior, web-browsing patterns, ordering patterns, and the like. Aclassifier may include a machine-learning ‘hypothesis’, ordiscrete-valued function, that is used to assign categorical classlabels to particular data points. In non-limiting illustrative examples,a classifier may relate to the identity and states of compatibleelements a user has previously ordered, considered ordering, and thelike, that may be a packet of data used to search or otherwise identifydeterminations by system 100 described herein. A classifier may includeat least a user activity datum classified to compatible element orderingbehaviors, for instance and without limitation, user activity data mayinclude a variety of “ordering behaviors” such as web-based searches forexercise equipment, gym memberships, cycling and running clubs, athleticapparel, and the like, wherein the classifier may describe the subset ofitems (compatible elements) and a corresponding identifier that relatesto the category the searches belong (corresponding user compatibleelement ordering behaviors) in the data.

Continuing in reference to FIG. 17, a classifier may be generated by aclassification machine-learning process, as described in further detailbelow. Classification machine-learning process may include amachine-learning process, as described above, such as a supervisedmachine-learning process. Classification machine-learning process mayaccept an input of a plurality of user fingerprint data 132 retrievedfrom a fingerprint database 128 and generate an output that is aclassifier relating to the data in the fingerprint database 128.Classification machine-learning process may store and/or retrieve suchdata and any classifier from fingerprint database 128.

Continuing in reference to FIG. 17, a classifier may include amachine-learning model, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. Computing device and/or another device may generate aclassifier using a classification machine-learning process performing aclassification algorithm, defined as a process whereby a computingdevice derives a classifier from training data. In non-limitingillustrative examples, such training data may be user fingerprint data132. Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 17, computing device may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 17, computing device may be configuredto generate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

Continuing in reference to FIG. 17, at step 1725, computing device isconfigured for identifying a plurality of compatible elements at thecurrent user location as a function of the condition of the user.Identifying the plurality of compatible elements at the current userlocation may include retrieving the plurality of compatible elements asa function of the fingerprint data contained in the classifier.Identifying a plurality of compatible elements may include generating,using a ranking machine-learning process, a plurality of compatibilitymetrics for the plurality of alternative compatible elements, whereineach compatibility metric quantifies the compatible element orderingbehavior for each compatible element as a function of the usercondition, and ranking the plurality of compatible elements as afunction of the plurality of compatibility metrics; this may beimplemented, without limitation, as described above in FIGS. 1-16.

Continuing in reference to FIG. 17, computing device may identify aplurality of compatible elements as a function of the current userlocation, wherein computing device may retrieve and/or identify thecompatible elements available to a user at a particular location. Thelocation may be associated, for instance and without limitation, with agrocery store or restaurant where computing device may identify aplurality of compatible elements as a function of the location and thediagnostic output. Computing device may then generate a ranking of theplurality of compatible elements using a ranking machine-learningprocess. Ranking machine-learning process may be any machine-learningalgorithm, process, or the like, performed as described above. Rankingmachine-learning process may accept an input that is the plurality ofcompatible elements and generate a compatibility metric for eachcompatible element using, for instance a scoring function, weightingmechanism, of the like.

Continuing in reference to FIG. 17, a “compatibility metric,” as used inthis disclosure, is a qualitative and/or quantitative metric thatquantifies the compatible element ordering behavior for a compatibleelement as a function of the user condition. A compatibility metric mayinclude a descriptor of the ‘compatibility’ of a compatible element witha user, wherein the compatibility may be expressed as a quantificationof how the compatible element will affect the user condition and howwell the compatible elements tracks to compatible element orderingbehavior. Ranking machine-learning process may take the plurality ofcompatible elements and rank the plurality of compatible elements as afunction of the compatibility metric. Ranking machine-learning processmay then generate a final output that is an indexed ranking of theplurality of compatible elements. The ranking may include a rankingwherein the highest ranked compatible element has the greatest positiveand/or adverse effect on the user condition. The ranking may include aranking that places the compatible element a user is most likely toorder with the highest ranking. The ranking may be a chronologicalranking, wherein the compatible elements are expected to be ordered insequence of when they should be obtained by the user, for instance, forthe greatest positive effect on user condition.

Continuing in reference to FIG. 17, at step 1730, computing device isconfigured for selecting at least a compatible element as a function ofthe fingerprint data. Selecting at least a compatible element as afunction of the fingerprint data may include filtering the plurality ofcompatible elements as a function of user fingerprint data; this may beimplemented, without limitation, as described above in FIGS. 1-16.

Continuing in reference to FIG. 17, filtering the plurality ofcompatible elements may include removing compatible elements that a useris not likely to order as a function of ordering behavior, user activitydata, and the like, as described in the user fingerprint data 132.Filtering the plurality of compatible elements may include removingcompatible elements that a user has recently ordered and is not likelyto order a second time within a particular frame of time. Filtering theplurality of compatible elements may include filtering based on currentuser location, wherein compatible elements not available at theestablish where a user is currently located are removed and/or added. Innon-limiting illustrative examples, a user may be in a fast-foodrestaurant where some of the plurality of compatible elements are notavailable and are filtered according. In further non-limitingillustrative examples, a current user location may indicate a user is ina department store wherein the computing device may filter compatibleelements that are not among the brands of product sold in the departmentstore. Those skilled in the art will appreciate, upon review of thisdisclosure in its entirety, that filtering of compatible elements may beperformed by computing device using a variety of criteria andcombinations of those criteria as a function of the user's condition,location, and ordering behaviors.

Continuing in reference to FIG. 17, at step 1735, computing device isconfigured for presenting, via a graphical user interface, at least acompatible element to a user device. Presenting at least a compatibleelement to a user device may include generating an audiovisualnotification, wherein the audiovisual notification alerts the user to atleast an adverse effect on the user condition. Presenting at least acompatible element to a user device may include prompting the user toorder at least a compatible element as a function of the current userlocation; this may be implemented, without limitation, as describedabove in FIGS. 1-16.

Continuing in reference to FIG. 17, a user device may be the same as acomputing device. User device may include a “smartphone”, laptop, tabletcomputer, internet-of-things (JOT) device, or the like. User device maypresent at least a compatible element to a user, including thecompatibility index, ranking, and any other determination associatedwith the compatible element as described herein. Presenting at least acompatible element to a user device may include generating for the useran audiovisual notification, which may alert the user to the compatibleelement. An “audiovisual notification,” as used in this disclosure, is anotification that alerts the user via an audio-visual presentation. Anaudiovisual notification may include textual alert, a graphic, avibration alert, a sound, or any other audio-visual notification, orcombination thereof, that a remote device may provide a user. Anaudiovisual notification may alert a user as a function of the currentuser location, for instance as a user is at a mall, as they enter andexit a variety of stores, the compatible elements may be filtered andtheir presentation may change as a function of the location.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 18 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1800 includes a processor 1804 and a memory1808 that communicate with each other, and with other components, via abus 1812. Bus 1812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1808 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1816 (BIOS), including basic routines thathelp to transfer information between elements within computer system1800, such as during start-up, may be stored in memory 1808. Memory 1808may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1800 may also include a storage device 1824. Examples ofa storage device (e.g., storage device 1824) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1824 may beconnected to bus 1812 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1824 (or one or more components thereof) may be removably interfacedwith computer system 1800 (e.g., via an external port connector (notshown)). Particularly, storage device 1824 and an associatedmachine-readable medium 1828 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1800. In one example,software 1820 may reside, completely or partially, withinmachine-readable medium 1828. In another example, software 1820 mayreside, completely or partially, within processor 1804.

Computer system 1800 may also include an input device 1832. In oneexample, a user of computer system 1800 may enter commands and/or otherinformation into computer system 1800 via input device 1832. Examples ofan input device 1832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1832may be interfaced to bus 1812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1812, and any combinations thereof. Input device 1832may include a touch screen interface that may be a part of or separatefrom display 1836, discussed further below. Input device 1832 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1800 via storage device 1824 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1840. A networkinterface device, such as network interface device 1840, may be utilizedfor connecting computer system 1800 to one or more of a variety ofnetworks, such as network 1844, and one or more remote devices 1848connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1844, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1820, etc.) may be communicated to and/or fromcomputer system 1800 via network interface device 1840.

Computer system 1800 may further include a video display adapter 1852for communicating a displayable image to a display device, such asdisplay device 1836. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1852 and display device 1836 maybe utilized in combination with processor 1804 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1800 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1812 via a peripheral interface 1856.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for using artificial intelligence toanalyze user activity data, the system comprising: a computing device,wherein the computing device is designed and configured to: receive froma user, at least a biological extraction and at least a user activitydatum; determine a current user location; generate a diagnostic engineconfigured to: generate a diagnostic output as a function of thebiological extraction wherein the diagnostic output comprises a usercondition configured to describe the effect of a variety of alimentaryelements on the user condition; retrieve, from a fingerprint database,at least a datum of user fingerprint data; identify a plurality ofcompatible elements at the current user location as a function of theuser condition by generating a plurality of compatibility metrics usinga ranking machine-learning process; select at least a compatible elementas a function of the fingerprint data, wherein selecting the at leastcompatible element comprises: retrieving at least a compatible elementsimilarity index value from a compatible element similarity index valuedatabase, wherein the at least compatible element similarity index valueis a value assigned to a compatible element indicating a degree ofsimilarity between a first compatible element and a second compatibleelement; and selecting at least a compatible element as a function ofthe compatible element similarity index value; and present, via agraphical user interface, the at least a compatible element to a userdevice.
 2. The system of claim 1, wherein receiving the at least a useractivity datum further comprises receiving user input via the graphicaluser interface.
 3. The system of claim 1, wherein the current userlocation includes an alimentary provider, and identifying the pluralityof compatible elements further comprises identifying the compatibleelements as a function of the alimentary provider.
 4. The system ofclaim 1, wherein generating the diagnostic output further comprises:training a diagnostic machine-learning model with training data, whereintraining data further comprises receiving a training set including aplurality of first data entries, each first data entry of the pluralityof first data entries including at least an element of biologicalextraction data correlated to a user condition; and generating adiagnostic output as a function of the trained diagnosticmachine-learning model and the biological extraction.
 5. The system ofclaim 1, wherein retrieving the at least a datum of user fingerprintdata further comprises: generating a classifier using a classificationmachine-learning process, wherein the classifier relates at least a useractivity datum classified to compatible element ordering behaviors; andretrieving the at least a datum of user fingerprint data as a functionof the at least a user activity datum and the classifier.
 6. The systemof claim 1, wherein identifying the plurality of compatible elements atthe current user location further comprises retrieving the plurality ofcompatible elements as a function of the fingerprint data.
 7. The systemof claim 1, wherein identifying the plurality of compatible elementsfurther comprises: generating, using a ranking machine-learning process,a plurality of compatibility metrics for the plurality of alternativecompatible elements, wherein each compatibility metric quantifies thecompatible element ordering behavior for each compatible element as afunction of the user condition; and ranking the plurality of compatibleelements as a function of the plurality of compatibility metrics.
 8. Thesystem of claim 1, wherein selecting the at least a compatible elementfurther comprises filtering the plurality of compatible elements as afunction of user fingerprint data.
 9. The system of claim 1, whereinpresenting the at least a compatible element to the user device furthercomprises generating an audiovisual notification, wherein theaudiovisual notification alerts the user to at least an adverse effecton the user condition.
 10. The system of claim 1, wherein presenting theat least a compatible element to the user device further comprisesprompting the user to order at least a compatible element as a functionof the user location.
 11. A method for using artificial intelligence toanalyze user activity data, the method comprising: receiving, by acomputing device, from a user, at least a biological extraction and atleast a user activity datum; determining, by the computing device, acurrent user location; generating, by the computing device, a diagnosticoutput as a function of the biological extraction, wherein thediagnostic output comprises user condition configured to describe theeffect of a variety of alimentary elements on the user condition;retrieving, by the computing device, from a fingerprint database, atleast a datum of user fingerprint data; identifying, by the computingdevice, a plurality of compatible elements at the current user locationas a function of the user condition by generating a plurality ofcompatibility metrics using a ranking machine-learning process;selecting, by the computing device, at least a compatible element as afunction of the fingerprint data wherein selecting the at leastcompatible element comprises: retrieving at least a compatible elementsimilarity index value from a compatible element similarity index valuedatabase, wherein the at least compatible element similarity index valueis a value assigned to a compatible element indicating a degree ofsimilarity between a first compatible element and a second compatibleelement; and selecting at least a compatible element as a function ofthe compatible element similarity index value; and presenting, by thecomputing device, via a graphical user interface, the at least acompatible element to a user device.
 12. The method of claim 11, whereinreceiving the at least a user activity datum further comprises receivinguser input via the graphical user interface.
 13. The method of claim 11,wherein the current user location includes an alimentary provider, andidentifying the plurality of compatible elements further comprisesidentifying the compatible elements as a function of the alimentaryprovider.
 14. The method of claim 11, wherein generating the diagnosticoutput further comprises: training a diagnostic machine-learning modelwith training data, wherein training data further comprises receiving atraining set including a plurality of first data entries, each firstdata entry of the plurality of first data entries including at least anelement of biological extraction data correlated to a user condition;and generating a diagnostic output as a function of the traineddiagnostic machine-learning model and the biological extraction.
 15. Themethod of claim 11, wherein retrieving the at least a datum of userfingerprint data further comprises: generating a classifier using aclassification machine-learning process, wherein the classifier relatesat least a user activity datum classified to compatible element orderingbehaviors; and retrieving the at least a datum of user fingerprint dataas a function of the at least a user activity datum and the classifier.16. The method of claim 11, wherein identifying the plurality ofcompatible elements at the current user location further comprisesretrieving the plurality of compatible elements as a function of thefingerprint data.
 17. The method of claim 11, wherein identifying theplurality of compatible elements further comprises: generating, using aranking machine-learning process, a plurality of compatibility metricsfor the plurality of alternative compatible elements, wherein eachcompatibility metric quantifies the compatible element ordering behaviorfor each compatible element as a function of the user condition; andranking the plurality of compatible elements as a function of theplurality of compatibility metrics.
 18. The method of claim 11, whereinselecting the at least a compatible element further comprises filteringthe plurality of compatible elements as a function of user fingerprintdata.
 19. The method of claim 11, wherein presenting the at least acompatible element to the user device further comprises generating anaudiovisual notification, wherein the audiovisual notification alertsthe user to at least an adverse effect on the user condition.
 20. Themethod of claim 11, wherein presenting the at least a compatible elementto the user device further comprises prompting the user to order atleast a compatible element as a function of the user location.